Nothing
R/ssfunctions.R
In smooth: Forecasting Using State Space Models
Defines functions
debugger
ssOutput
ICFunction
likelihoodFunction
ssXreg
ssForecaster
ssIntervals
ssFitter
ssAutoInput
ssInput
utils::globalVariables(c("h","holdout","orders","lags","transition","measurement","multisteps","ot",
"obsInSample","obsAll","obsStates","obsNonzero","obsZero","pFitted","loss",
"CF","Etype","Ttype","Stype","matxt","matFX","vecgX","xreg","matvt","nExovars",
"matat","errors","nParam","interval","intervalType","level","model","oesmodel",
"imodel","constant","AR","MA","y","yFitted","cumulative","rounded"));
##### *Checker of input of basic functions* #####
ssInput <- function(smoothType=c("es","gum","ces","ssarima","smoothC"),...){
# This is universal function needed in order to check the passed arguments to es(), gum(),
# ces() and ssarima()
smoothType <- smoothType[1];
ellipsis <- list(...);
ParentEnvironment <- ellipsis[['ParentEnvironment']];
##### silent #####
silent <- silent[1];
# Fix for cases with TRUE/FALSE.
if(!is.logical(silent)){
if(all(silent!=c("none","all","graph","legend","output","debugging","n","a","g","l","o","d"))){
warning(paste0("Sorry, I have no idea what 'silent=",silent,"' means. Switching to 'none'."),
call.=FALSE);
silent <- "none";
}
silent <- substring(silent,1,1);
}
silentValue <- silent;
if(silentValue==FALSE | silentValue=="n"){
silentText <- FALSE;
silentGraph <- FALSE;
silentLegend <- FALSE;
}
else if(silentValue==TRUE | silentValue=="a"){
silentText <- TRUE;
silentGraph <- TRUE;
silentLegend <- TRUE;
}
else if(silentValue=="g"){
silentText <- FALSE;
silentGraph <- TRUE;
silentLegend <- TRUE;
}
else if(silentValue=="l"){
silentText <- FALSE;
silentGraph <- FALSE;
silentLegend <- TRUE;
}
else if(silentValue=="o"){
silentText <- TRUE;
silentGraph <- FALSE;
silentLegend <- FALSE;
}
else if(silentValue=="d"){
silentText <- TRUE;
silentGraph <- FALSE;
silentLegend <- FALSE;
}
# Check what was passed as a horizon
if(h<=0){
warning(paste0("You have set forecast horizon equal to ",h,". We hope you know, what you are doing."),
call.=FALSE);
if(h<0){
warning("And by the way, we can't do anything with negative horizon, so we will set it equal to zero.",
call.=FALSE);
h <- 0;
}
}
##### data #####
if(any(is.smooth.sim(y))){
y <- y$data;
}
else if(any(class(y)=="Mdata")){
h <- y$h;
holdout <- TRUE;
y <- ts(c(y$x,y$xx),start=start(y$x),frequency=frequency(y$x));
}
if(!is.numeric(y)){
stop("The provided data is not a vector or ts object! Can't construct any model!", call.=FALSE);
}
if(!is.null(ncol(y))){
if(ncol(y)>1){
stop("The provided data is not a vector! Can't construct any model!", call.=FALSE);
}
}
# Check the data for NAs
if(any(is.na(y))){
if(!silentText){
warning("Data contains NAs. These observations will be substituted by zeroes.",call.=FALSE);
}
y[is.na(y)] <- 0;
}
# Define obs, the number of observations of in-sample
obsInSample <- length(y) - holdout*h;
# Define obsAll, the overal number of observations (in-sample + holdout)
obsAll <- length(y) + (1 - holdout)*h;
# If obsInSample is negative, this means that we can't do anything...
if(obsInSample<=0){
stop("Not enough observations in sample.",call.=FALSE);
}
# Define the actual values
yInSample <- matrix(y[1:obsInSample],obsInSample,1);
dataFreq <- frequency(y);
dataStart <- start(y);
yForecastStart <- time(y)[obsInSample]+deltat(y);
# Number of parameters to estimate / provided
parametersNumber <- matrix(0,2,4,
dimnames=list(c("Estimated","Provided"),
c("nParamInternal","nParamXreg","nParamOccurrence","nParamAll")));
if(any(smoothType==c("es","oes"))){
##### model for ES #####
if(!is.character(model)){
stop(paste0("Something strange is provided instead of character object in model: ",
paste0(model,collapse=",")),call.=FALSE);
}
# Predefine models pool for a model selection
modelsPool <- NULL;
# Deal with the list of models. Check what has been provided. Stop if there is a mistake.
if(length(model)>1){
if(any(nchar(model)>4)){
stop(paste0("You have defined strange model(s) in the pool: ",
paste0(model[nchar(model)>4],collapse=",")),call.=FALSE);
}
else if(any(substr(model,1,1)!="A" & substr(model,1,1)!="M" & substr(model,1,1)!="C")){
stop(paste0("You have defined strange model(s) in the pool: ",
paste0(model[substr(model,1,1)!="A" & substr(model,1,1)!="M"],collapse=",")),
call.=FALSE);
}
else if(any(substr(model,2,2)!="N" & substr(model,2,2)!="A" &
substr(model,2,2)!="M" & substr(model,2,2)!="C")){
stop(paste0("You have defined strange model(s) in the pool: ",
paste0(model[substr(model,2,2)!="N" & substr(model,2,2)!="A" &
substr(model,2,2)!="M"],collapse=",")),call.=FALSE);
}
else if(any(substr(model,3,3)!="N" & substr(model,3,3)!="A" &
substr(model,3,3)!="M" & substr(model,3,3)!="d" & substr(model,3,3)!="C")){
stop(paste0("You have defined strange model(s) in the pool: ",
paste0(model[substr(model,3,3)!="N" & substr(model,3,3)!="A" &
substr(model,3,3)!="M" & substr(model,3,3)!="d"],collapse=",")),
call.=FALSE);
}
else if(any(nchar(model)==4 & substr(model,4,4)!="N" & substr(model,4,4)!="A" &
substr(model,4,4)!="M" & substr(model,4,4)!="C")){
stop(paste0("You have defined strange model(s) in the pool: ",
paste0(model[nchar(model)==4 & substr(model,4,4)!="N" &
substr(model,4,4)!="A" & substr(model,4,4)!="M"],collapse=",")),
call.=FALSE);
}
else{
modelsPoolCombiner <- (substr(model,1,1)=="C" | substr(model,2,2)=="C" |
substr(model,3,3)=="C" | substr(model,4,4)=="C");
modelsPool <- model[!modelsPoolCombiner];
modelsPool <- unique(modelsPool);
if(any(modelsPoolCombiner)){
if(any(substr(model,nchar(model),nchar(model))!="N")){
model <- "CCC";
}
else{
model <- "CCN";
}
}
else{
model <- c("Z","Z","Z");
if(all(substr(modelsPool,nchar(modelsPool),nchar(modelsPool))=="N")){
model[3] <- "N";
}
if(all(substr(modelsPool,2,2)=="N")){
model[2] <- "N";
}
model <- paste0(model,collapse="");
}
}
}
# If chosen model is "AAdN" or anything like that, we are taking the appropriate values
if(nchar(model)==4){
Etype <- substring(model,1,1);
Ttype <- substring(model,2,2);
Stype <- substring(model,4,4);
damped <- TRUE;
if(substring(model,3,3)!="d"){
message(paste0("You have defined a strange model: ",model));
sowhat(model);
model <- paste0(Etype,Ttype,"d",Stype);
}
}
else if(nchar(model)==3){
Etype <- substring(model,1,1);
Ttype <- substring(model,2,2);
Stype <- substring(model,3,3);
if(any(Ttype==c("Z","X","Y"))){
damped <- TRUE;
}
else{
damped <- FALSE;
}
}
else{
message(paste0("You have defined a strange model: ",model));
sowhat(model);
message("Switching to 'ZZZ'");
model <- "ZZZ";
Etype <- "Z";
Ttype <- "Z";
Stype <- "Z";
damped <- TRUE;
}
# Define if we want to select or combine models... or do none of the above.
if(is.null(modelsPool)){
if(any(unlist(strsplit(model,""))=="C")){
modelDo <- "combine";
if(Etype=="C"){
Etype <- "Z";
}
if(Ttype=="C"){
Ttype <- "Z";
}
if(Stype=="C"){
Stype <- "Z";
}
}
else if(any(unlist(strsplit(model,""))=="Z") |
any(unlist(strsplit(model,""))=="X") |
any(unlist(strsplit(model,""))=="Y")){
modelDo <- "select";
}
else{
modelDo <- "estimate";
}
if(Etype=="X"){
Etype <- "A";
}
else if(Etype=="Y"){
Etype <- "M";
}
}
else{
if(any(unlist(strsplit(model,""))=="C")){
modelDo <- "combine";
}
else{
modelDo <- "select";
}
}
### Check error type
if(all(Etype!=c("Z","X","Y","A","M","C"))){
warning(paste0("Wrong error type: ",Etype,". Should be 'Z', 'X', 'Y', 'A' or 'M'.\n",
"Changing to 'Z'"),call.=FALSE);
Etype <- "Z";
modelDo <- "select";
}
### Check trend type
if(all(Ttype!=c("Z","X","Y","N","A","M","C"))){
warning(paste0("Wrong trend type: ",Ttype,". Should be 'Z', 'X', 'Y', 'N', 'A' or 'M'.\n",
"Changing to 'Z'"),call.=FALSE);
Ttype <- "Z";
modelDo <- "select";
}
}
else if(any(smoothType==c("ssarima","msarima"))){
##### Orders and lags for ssarima #####
if(any(is.complex(c(ar.orders,i.orders,ma.orders,lags)))){
stop("Come on! Be serious! This is ARIMA, not CES!",call.=FALSE);
}
if(any(c(ar.orders,i.orders,ma.orders)<0)){
stop("Funny guy! How am I gonna construct a model with negative order?",call.=FALSE);
}
if(any(c(lags)<0)){
stop("Right! Why don't you try complex lags then, mister smart guy?",call.=FALSE);
}
# If there are zero lags, drop them
if(any(lags==0)){
ar.orders <- ar.orders[lags!=0];
i.orders <- i.orders[lags!=0];
ma.orders <- ma.orders[lags!=0];
lags <- lags[lags!=0];
}
if(any(lags>48) & (smoothType=="ssarima")){
warning(paste0("SSARIMA is quite slow with lags greater than 48. ",
"It is recommended to use MSARIMA in this case instead."),
call.=FALSE);
}
# Define maxorder and make all the values look similar (for the polynomials)
maxorder <- max(length(ar.orders),length(i.orders),length(ma.orders));
if(length(ar.orders)!=maxorder){
ar.orders <- c(ar.orders,rep(0,maxorder-length(ar.orders)));
}
if(length(i.orders)!=maxorder){
i.orders <- c(i.orders,rep(0,maxorder-length(i.orders)));
}
if(length(ma.orders)!=maxorder){
ma.orders <- c(ma.orders,rep(0,maxorder-length(ma.orders)));
}
if((length(lags)!=length(ar.orders)) & (length(lags)!=length(i.orders)) & (length(lags)!=length(ma.orders))){
stop("Seasonal lags do not correspond to any element of SARIMA",call.=FALSE);
}
# If zeroes are defined for some orders, drop them.
if(any((ar.orders + i.orders + ma.orders)==0)){
orders2leave <- (ar.orders + i.orders + ma.orders)!=0;
if(all(!orders2leave)){
orders2leave <- lags==min(lags);
}
ar.orders <- ar.orders[orders2leave];
i.orders <- i.orders[orders2leave];
ma.orders <- ma.orders[orders2leave];
lags <- lags[orders2leave];
}
# Get rid of duplicates in lags
if(length(unique(lags))!=length(lags)){
if(dataFreq!=1){
warning(paste0("'lags' variable contains duplicates: (",paste0(lags,collapse=","),
"). Getting rid of some of them."),call.=FALSE);
}
lagsNew <- unique(lags);
arOrdersNew <- iOrdersNew <- maOrdersNew <- lagsNew;
for(i in 1:length(lagsNew)){
arOrdersNew[i] <- max(ar.orders[which(lags==lagsNew[i])]);
iOrdersNew[i] <- max(i.orders[which(lags==lagsNew[i])]);
maOrdersNew[i] <- max(ma.orders[which(lags==lagsNew[i])]);
}
ar.orders <- arOrdersNew;
i.orders <- iOrdersNew;
ma.orders <- maOrdersNew;
lags <- lagsNew;
}
ARValue <- AR;
# Check the provided AR matrix / vector
if(!is.null(ARValue)){
if((!is.numeric(ARValue) | !is.vector(ARValue)) & !is.matrix(ARValue)){
warning(paste0("AR should be either vector or matrix. You have provided something strange...\n",
"AR will be estimated."),call.=FALSE);
ARRequired <- AREstimate <- TRUE;
ARValue <- NULL;
}
else{
if(is.matrix(ARValue)){
ARLength <- length(ARValue[ARValue!=0]);
}
else{
ARLength <- length(ARValue);
}
if(sum(ar.orders)!=ARLength){
warning(paste0("Wrong number of non-zero elements of AR. Should be ",sum(ar.orders),
" instead of ",length(ARValue[ARValue!=0]),".\n",
"AR will be estimated."),call.=FALSE);
ARRequired <- AREstimate <- TRUE;
ARValue <- NULL;
}
else{
if(is.matrix(ARValue)){
ARValue <- ARValue[ARValue!=0];
}
AREstimate <- FALSE;
ARRequired <- TRUE;
parametersNumber[2,1] <- parametersNumber[2,1] + length(ARValue);
}
}
}
else{
if(all(ar.orders==0)){
ARRequired <- AREstimate <- FALSE;
}
else{
ARRequired <- AREstimate <- TRUE;
}
}
MAValue <- MA;
# Check the provided MA matrix / vector
if(!is.null(MAValue)){
if((!is.numeric(MAValue) | !is.vector(MAValue)) & !is.matrix(MAValue)){
warning(paste0("MA should be either vector or matrix. You have provided something strange...\n",
"MA will be estimated."),call.=FALSE);
MARequired <- MAEstimate <- TRUE;
MAValue <- NULL;
}
else{
if(is.matrix(MAValue)){
MALength <- length(MAValue[MAValue!=0]);
}
else{
MALength <- length(MAValue);
}
if(sum(ma.orders)!=MALength){
warning(paste0("Wrong number of non-zero elements of MA. Should be ",sum(ma.orders),
" instead of ",length(MAValue[MAValue!=0]),".\n",
"MA will be estimated."),call.=FALSE);
MARequired <- MAEstimate <- TRUE;
MAValue <- NULL;
}
else{
if(is.matrix(MAValue)){
MAValue <- MAValue[MAValue!=0];
}
MAEstimate <- FALSE;
MARequired <- TRUE;
parametersNumber[2,1] <- parametersNumber[2,1] + length(MAValue);
}
}
}
else{
if(all(ma.orders==0)){
MARequired <- MAEstimate <- FALSE;
}
else{
MARequired <- MAEstimate <- TRUE;
}
}
constantValue <- constant;
# Check the provided constant
if(is.numeric(constantValue)){
constantEstimate <- FALSE;
constantRequired <- TRUE;
parametersNumber[2,1] <- parametersNumber[2,1] + 1;
}
else if(is.logical(constantValue)){
constantRequired <- constantEstimate <- constantValue;
constantValue <- NULL;
}
# Number of components to use
nComponents <- max(ar.orders %*% lags + i.orders %*% lags,ma.orders %*% lags);
# If there is no constant and there are no orders
if(nComponents==0 & !constantRequired){
constantValue <- 0;
constantRequired[] <- TRUE
nComponenst <- 1;
}
nonZeroARI <- matrix(1,ncol=2);
nonZeroMA <- matrix(1,ncol=2);
lagsModel <- matrix(rep(1,nComponents),ncol=1);
if(constantRequired){
lagsModel <- rbind(lagsModel,1);
}
lagsModelMax <- 1;
if(obsInSample < nComponents){
warning(paste0("In-sample size is ",obsInSample,", while number of components is ",nComponents,
". Cannot fit the model."),call.=FALSE)
stop("Not enough observations for such a complicated model.",call.=FALSE);
}
}
else if(smoothType=="ces"){
# If the user typed wrong seasonality, use the "Full" instead
if(all(seasonality!=c("n","s","p","f","none","simple","partial","full"))){
warning(paste0("Wrong seasonality type: '",seasonality, "'. Changing to 'full'"), call.=FALSE);
seasonality <- "f";
}
seasonality <- substring(seasonality[1],1,1);
}
if(any(smoothType==c("es","oes"))){
modelIsSeasonal <- Stype!="N";
# Check if the data is ts-object
if(!is.ts(y) & modelIsSeasonal){
if(!silentText){
message("The provided data is not ts object. Only non-seasonal models are available.");
}
Stype <- "N";
modelIsSeasonal <- FALSE;
substr(model,nchar(model),nchar(model)) <- "N";
}
### Check seasonality type
if(all(Stype!=c("Z","X","Y","N","A","M","C"))){
warning(paste0("Wrong seasonality type: ",Stype,". Should be 'Z', 'X', 'Y', 'N', 'A' or 'M'.",
"Setting to 'Z'."),call.=FALSE);
if(dataFreq==1){
Stype <- "N";
modelIsSeasonal <- FALSE;
}
else{
Stype <- "Z";
modelDo <- "select";
}
}
if(all(modelIsSeasonal,dataFreq==1)){
if(all(Stype!=c("Z","X","Y"))){
warning(paste0("Cannot build the seasonal model on data with frequency 1.\n",
"Switching to non-seasonal model: ETS(",substring(model,1,nchar(model)-1),"N)"));
}
Stype <- "N";
modelIsSeasonal <- FALSE;
}
# Check the pool of models to combine if it was decided that the data is not seasonal
if(!modelIsSeasonal && !is.null(modelsPool)){
modelsPool <- modelsPool[substr(modelsPool,nchar(modelsPool),nchar(modelsPool))=="N"];
}
}
else if(smoothType=="sma"){
lagsModelMax <- 1;
if(is.null(order)){
nParamMax <- obsInSample;
}
else{
nParamMax <- order;
}
}
else{
lagsModelMax <- 1;
nParamMax <- 0;
}
# Make sure that y is ts
if(!is.ts(y)){
y <- ts(y);
}
##### Lags and components for GUM #####
if(smoothType=="gum"){
if(any(is.complex(c(orders,lags)))){
stop("Complex values? Right! Come on! Be real!",call.=FALSE);
}
if(any(c(orders)<0)){
stop("Funny guy! How am I gonna construct a model with negative orders?",call.=FALSE);
}
if(any(c(lags)<0)){
stop("Right! Why don't you try complex lags then, mister smart guy?",call.=FALSE);
}
if(length(orders) != length(lags)){
stop(paste0("The length of 'lags' (",length(lags),
") differes from the length of 'orders' (",length(orders),")."), call.=FALSE);
}
# If there are zero lags, drop them
if(any(lags==0)){
orders <- orders[lags!=0];
lags <- lags[lags!=0];
}
# If zeroes are defined for some orders, drop them.
if(any(orders==0)){
lags <- lags[orders!=0];
orders <- orders[orders!=0];
}
# Get rid of duplicates in lags
if(length(unique(lags))!=length(lags)){
lagsNew <- unique(lags);
ordersNew <- lagsNew;
for(i in 1:length(lagsNew)){
ordersNew[i] <- max(orders[which(lags==lagsNew[i])]);
}
orders <- ordersNew;
lags <- lagsNew;
}
lagsModel <- matrix(rep(lags,times=orders),ncol=1);
lagsModelMax <- max(lagsModel);
nComponents <- sum(orders);
type <- substr(type[1],1,1);
if(type=="m"){
if(any(yInSample<=0)){
warning("Multiplicative model can only be used on positive data. Switching to the additive one.",
call.=FALSE);
modelIsMultiplicative <- FALSE;
type <- "a";
}
else{
yInSample <- log(yInSample);
modelIsMultiplicative <- TRUE;
}
}
else{
modelIsMultiplicative <- FALSE;
}
}
else if(any(smoothType==c("es","oes"))){
lagsModelMax <- dataFreq * modelIsSeasonal + 1 * (!modelIsSeasonal);
}
else if(smoothType=="ces"){
a <- list(value=a);
b <- list(value=b);
if(is.null(a$value)){
a$estimate <- TRUE;
}
else{
a$estimate <- FALSE;
if(!is.null(a$value)){
parametersNumber[2,1] <- parametersNumber[2,1] + length(Re(a$value)) + length(Im(a$value));
}
}
if(all(is.null(b$value),any(seasonality==c("p","f")))){
b$estimate <- TRUE;
}
else{
b$estimate <- FALSE;
if(!is.null(b$value)){
parametersNumber[2,1] <- parametersNumber[2,1] + length(Re(b$value)) + length(Im(b$value));
}
}
# Define lags, number of components and number of parameters
if(seasonality=="n"){
# No seasonality
lagsModelMax <- 1;
lagsModel <- c(1,1);
# Define the number of all the parameters (smoothing parameters + initial states). Used in AIC mainly!
nComponents <- 2;
a$number <- 2;
b$number <- 0;
}
else if(seasonality=="s"){
# Simple seasonality, lagged CES
lagsModelMax <- dataFreq;
lagsModel <- c(lagsModelMax,lagsModelMax);
nComponents <- 2;
a$number <- 2;
b$number <- 0;
}
else if(seasonality=="p"){
# Partial seasonality with a real part only
lagsModelMax <- dataFreq;
lagsModel <- c(1,1,lagsModelMax);
nComponents <- 3;
a$number <- 2;
b$number <- 1;
}
else if(seasonality=="f"){
# Full seasonality with both real and imaginary parts
lagsModelMax <- dataFreq;
lagsModel <- c(1,1,lagsModelMax,lagsModelMax);
nComponents <- 4;
a$number <- 2;
b$number <- 2;
}
}
#### This is a temporary thing. If the function works, we will do that properly ####
else if(smoothType=="msarima"){
# Define the non-zero values. This is done via the calculation of orders of polynomials
ariValues <- list(NA);
maValues <- list(NA);
for(i in 1:length(lags)){
ariValues[[i]] <- c(0,min(1,ar.orders[i]):ar.orders[i])
if(i.orders[i]!=0){
ariValues[[i]] <- c(ariValues[[i]],1:i.orders[i]+ar.orders[i]);
}
ariValues[[i]] <- unique(ariValues[[i]] * lags[i]);
maValues[[i]] <- unique(c(0,min(1,ma.orders[i]):ma.orders[i]) * lags[i]);
}
# Produce ARI polynomials
ariLengths <- unlist(lapply(ariValues,length));
ariPolynomial <- array(0,ariLengths);
for(i in 1:length(ariValues)){
if(i==1){
ariPolynomial <- ariPolynomial + array(ariValues[[i]], ariLengths);
}
else{
ariPolynomial <- ariPolynomial + array(rep(ariValues[[i]],each=prod(ariLengths[1:(i-1)])),
ariLengths);
}
}
# Produce MA polynomials
maLengths <- unlist(lapply(maValues,length));
maPolynomial <- array(0,maLengths);
for(i in 1:length(maValues)){
if(i==1){
maPolynomial <- maPolynomial + array(maValues[[i]], maLengths);
}
else{
maPolynomial <- maPolynomial + array(rep(maValues[[i]],each=prod(maLengths[1:(i-1)])),
maLengths);
}
}
# What are the non-zero ARI and MA polynomials?
### What are their positions in transition matrix?
nonZeroARI <- unique(matrix(c(ariPolynomial)[-1],ncol=1));
nonZeroMA <- unique(matrix(c(maPolynomial)[-1],ncol=1));
nonZeroComponents <- sort(unique(c(nonZeroARI,nonZeroMA)));
nonZeroARI <- cbind(nonZeroARI,which(nonZeroComponents %in% nonZeroARI)-1);
nonZeroMA <- cbind(nonZeroMA,which(nonZeroComponents %in% nonZeroMA)-1);
nComponents <- length(nonZeroComponents);
if(obsInSample < nComponents){
warning(paste0("In-sample size is ",obsInSample,", while number of components is ",nComponents,
". Cannot fit the model."),call.=FALSE)
stop("Not enough observations for such a complicated model.",call.=FALSE);
}
lagsModel <- matrix(nonZeroComponents,ncol=1);
if(constantRequired){
lagsModel <- rbind(lagsModel,1);
}
lagsModelMax <- max(lagsModel);
}
##### obsStates #####
# Define the number of rows that should be in the matvt
obsStates <- max(obsAll + lagsModelMax, obsInSample + 2*lagsModelMax);
##### bounds #####
bounds <- substring(bounds[1],1,1);
if(all(bounds!=c("n","a","r","u"))){
warning("Strange bounds are defined. Switching to 'admissible'.",call.=FALSE);
bounds <- "a";
}
if(bounds=="n"){
warning("You have defined bounds='none'. ",
"This is dangerous and might lead to an unstable model or even break the function. ",
"Hopefully, you know what you are doing :).",
call.=FALSE);
}
##### Information Criteria #####
ic <- ic[1];
if(all(ic!=c("AICc","AIC","BIC","BICc"))){
warning(paste0("Strange type of information criteria defined: ",ic,". Switching to 'AICc'."),
call.=FALSE);
ic <- "AICc";
}
##### Loss function type #####
loss <- loss[1];
if(any(loss==c("MSEh","TMSE","GTMSE","MSCE","MAEh","TMAE","GTMAE","MACE",
"HAMh","THAM","GTHAM","CHAM",
"GPL","aMSEh","aTMSE","aGTMSE","aGPL"))){
multisteps <- TRUE;
}
else if(any(loss==c("likelihood","MSE","MAE","HAM","Rounded"))){
multisteps <- FALSE;
}
else{
if(loss=="MSTFE"){
warning(paste0("This estimator has recently been renamed from \"MSTFE\" to \"TMSE\". ",
"Please, use the new name."),call.=FALSE);
multisteps <- TRUE;
loss <- "TMSE";
}
else if(loss=="GMSTFE"){
warning(paste0("This estimator has recently been renamed from \"GMSTFE\" to \"GTMSE\". ",
"Please, use the new name."),call.=FALSE);
multisteps <- TRUE;
loss <- "GTMSE";
}
else if(loss=="aMSTFE"){
warning(paste0("This estimator has recently been renamed from \"aMSTFE\" to \"aTMSE\". ",
"Please, use the new name."),call.=FALSE);
multisteps <- TRUE;
loss <- "aTMSE";
}
else if(loss=="aGMSTFE"){
warning(paste0("This estimator has recently been renamed from \"aGMSTFE\" to \"aGTMSE\". ",
"Please, use the new name."),call.=FALSE);
multisteps <- TRUE;
loss <- "aGTMSE";
}
else{
warning(paste0("Strange loss function specified: ",loss,". Switching to 'MSE'."),call.=FALSE);
loss <- "MSE";
multisteps <- FALSE;
}
}
lossOriginal <- loss;
##### interval, intervalType, level #####
#intervalType <- substring(intervalType[1],1,1);
intervalType <- interval[1];
# Check the provided type of interval
if(is.logical(intervalType)){
if(intervalType){
intervalType <- "p";
}
else{
intervalType <- "none";
}
}
if(all(intervalType!=c("p","l","s","n","a","sp","np","none","parametric","likelihood","semiparametric","nonparametric"))){
warning(paste0("Wrong type of interval: '",intervalType, "'. Switching to 'parametric'."),call.=FALSE);
intervalType <- "p";
}
if(any(intervalType==c("none","n"))){
intervalType <- "n";
interval <- FALSE;
}
else if(any(intervalType==c("parametric","p"))){
intervalType <- "p";
interval <- TRUE;
}
else if(any(intervalType==c("semiparametric","sp"))){
intervalType <- "sp";
interval <- TRUE;
}
else if(any(intervalType==c("nonparametric","np"))){
intervalType <- "np";
interval <- TRUE;
}
else if(any(intervalType==c("likelihood","l"))){
intervalType <- "l";
interval <- TRUE;
}
else{
interval <- TRUE;
}
if(level>1){
level <- level / 100;
}
##### Occurrence part of the model #####
if(is.oes(occurrence)){
occurrenceModel <- occurrence;
occurrence <- occurrenceModel$occurrence;
occurrenceModelProvided <- TRUE;
}
else if(is.list(occurrence)){
warning(paste0("occurrence is not of the class oes. ",
"We will try to extract the type of model, but cannot promise anything."),
call.=FALSE);
occurrenceModel <- modelType(occurrence);
occurrence <- occurrence$occurrence;
occurrenceModelProvided <- FALSE;
}
else if(is.null(occurrence)){
occurrence <- "none";
occurrenceModel <- "MNN";
occurrenceModelProvided <- FALSE;
}
else{
if(is.null(oesmodel) || is.na(oesmodel)){
occurrenceModel <- "MNN";
}
else{
occurrenceModel <- oesmodel;
}
occurrenceModelProvided <- FALSE;
}
if(smoothType!="oes"){
if(is.numeric(occurrence)){
# If it is data, then it should either correspond to the whole sample (in-sample + holdout)
# or be equal to forecating horizon.
if(any(occurrence!=1) && all(length(c(occurrence))!=c(h,obsAll))){
warning(paste0("Length of the provided future occurrences is ",length(c(occurrence)),
" while length of forecasting horizon is ",h,".\n",
"Where should we plug in the future occurences anyway?\n",
"Switching to occurrence='fixed'."),call.=FALSE);
occurrence <- "f";
ot <- (yInSample!=0)*1;
obsNonzero <- sum(ot);
obsZero <- obsInSample - obsNonzero;
yot <- matrix(yInSample[yInSample!=0],obsNonzero,1);
pFitted <- matrix(mean(ot),obsInSample,1);
pForecast <- matrix(1,h,1);
nParamOccurrence <- 1;
}
else if(all(occurrence==1)){
obsNonzero <- obsInSample;
obsZero <- 0;
pFitted <- ot <- rep(1,obsInSample);
yot <- yInSample;
pForecast <- matrix(1,h,1);
nParamOccurrence <- 0;
occurrence <- "n";
occurrenceModelProvided <- FALSE;
}
else{
if(any(occurrence<0,occurrence>1)){
warning(paste0("Parameter 'occurrence' should contain values between zero and one.\n",
"Converting to appropriate vector."),call.=FALSE);
occurrence <- (occurrence!=0)*1;
}
ot <- (yInSample!=0)*1;
obsNonzero <- sum(ot);
obsZero <- obsInSample - obsNonzero;
yot <- matrix(yInSample[yInSample!=0],obsNonzero,1);
if(length(occurrence)==obsAll){
pFitted <- occurrence[1:obsInSample];
pForecast <- occurrence[(obsInSample+1):(obsInSample+h)];
}
else{
pFitted <- matrix(ot,obsInSample,1);
pForecast <- matrix(occurrence,h,1);
}
# "p" stand for "provided", meaning that we have been provided the future data
occurrence <- "p";
nParamOccurrence <- 0;
}
}
else{
occurrence <- occurrence[1];
if(all(occurrence!=c("n","a","f","g","o","i","d",
"none","auto","fixed","general","odds-ratio","inverse-odds-ratio","direct"))){
warning(paste0("Strange type of occurrence model defined: '",occurrence,
"'. Switching to 'fixed'."),
call.=FALSE);
occurrence <- "f";
}
occurrence <- substring(occurrence[1],1,1);
environment(intermittentParametersSetter) <- environment();
intermittentParametersSetter(occurrence,ParentEnvironment=environment());
}
# If the data is not occurrence, let's assume that the parameter was switched unintentionally.
if(all(ot==1) & all(occurrence!=c("n","p","provided"))){
occurrence <- "n";
occurrenceModelProvided <- FALSE;
}
if(occurrenceModelProvided){
parametersNumber[2,3] <- nparam(occurrenceModel);
}
}
else{
obsNonzero <- obsInSample;
obsZero <- 0;
}
if(any(smoothType==c("es"))){
# Check if multiplicative models can be fitted
allowMultiplicative <- !((any(yInSample<=0) && occurrence=="n") |
(occurrence!="n" && any(yInSample<0)));
# If non-positive values are present, check if data is intermittent,
# if negatives are here, switch to additive models
if(!allowMultiplicative){
if(any(Etype==c("M","Y"))){
warning(paste0("Can't apply multiplicative model to non-positive data. ",
"Switching error type to 'A'"), call.=FALSE);
Etype <- ifelse(Etype=="M","A","X");
}
if(any(Ttype==c("M","Y"))){
warning(paste0("Can't apply multiplicative model to non-positive data. ",
"Switching trend type to 'A'"), call.=FALSE);
Ttype <- ifelse(Ttype=="M","A","X");
}
if(any(Stype==c("M","Y"))){
warning(paste0("Can't apply multiplicative model to non-positive data. ",
"Switching seasonality type to 'A'"), call.=FALSE);
Stype <- ifelse(Stype=="M","A","X");
}
if(!is.null(modelsPool)){
if(any(c(substr(modelsPool,1,1),
substr(modelsPool,2,2),
substr(modelsPool,nchar(modelsPool),nchar(modelsPool)))=="M")){
warning(paste0("Can't apply multiplicative model to non-positive data. ",
"Switching to additive."), call.=FALSE);
substr(modelsPool,1,1)[substr(modelsPool,1,1)=="M"] <- "A";
substr(modelsPool,2,2)[substr(modelsPool,2,2)=="M"] <- "A";
substr(modelsPool,nchar(modelsPool),
nchar(modelsPool))[substr(modelsPool,nchar(modelsPool),
nchar(modelsPool))=="M"] <- "A";
}
}
}
# Check the pool of models. Make sure that there are no duplicates
if(!is.null(modelsPool)){
modelsPool <- unique(modelsPool);
if(is.null(modelsPool)){
stop(paste0("Cannot combine the models, your pool is empty. ",
"Please, check the vector of models you provided."),
call.=FALSE);
}
}
}
if(any(smoothType==c("es","gum","oes"))){
##### persistence for ES & GUM #####
if(!is.null(persistence)){
if((!is.numeric(persistence) | !is.vector(persistence)) & !is.matrix(persistence)){
warning(paste0("Persistence is not a numeric vector!\n",
"Changing to estimation of persistence vector values."),call.=FALSE);
persistence <- NULL;
persistenceEstimate <- TRUE;
}
else{
if(any(smoothType==c("es","oes"))){
if(modelDo!="estimate"){
warning(paste0("Predefined persistence vector can only be used with ",
"preselected ETS model.\n",
"Changing to estimation of persistence vector values."),call.=FALSE);
persistence <- NULL;
persistenceEstimate <- TRUE;
}
else{
if(length(persistence)>3){
warning(paste0("Length of persistence vector is wrong! ",
"It should not be greater than 3.\n",
"Changing to estimation of persistence vector values."),
call.=FALSE);
persistence <- NULL;
persistenceEstimate <- TRUE;
}
else{
if(length(persistence)!=(1 + (Ttype!="N") + (modelIsSeasonal))){
warning(paste0("Wrong length of persistence vector. ",
"Should be ",(1 + (Ttype!="N") + (modelIsSeasonal)),
" instead of ",length(persistence),".\n",
"Changing to estimation of persistence vector values."),
call.=FALSE);
persistence <- NULL;
persistenceEstimate <- TRUE;
}
else{
persistence <- as.vector(persistence);
persistenceEstimate <- FALSE;
parametersNumber[2,1] <- parametersNumber[2,1] + length(persistence);
# bounds <- "n";
}
}
}
}
else if(smoothType=="gum"){
if(length(persistence) != nComponents){
warning(paste0("Wrong length of persistence vector. Should be ",nComponents,
" instead of ",length(persistence),".\n",
"Changing to estimation of persistence vector values."),call.=FALSE);
persistence <- NULL;
persistenceEstimate <- TRUE;
}
else{
persistenceEstimate <- FALSE;
parametersNumber[2,1] <- parametersNumber[2,1] + length(persistence);
}
}
}
}
else{
persistenceEstimate <- TRUE;
}
}
##### initials ####
# initial type can be: "o" - optimal, "b" - backcasting, "p" - provided.
initialValue <- initial;
if(is.character(initialValue)){
initialValue <- substring(initialValue[1],1,1);
if(all(initialValue!=c("o","b","p"))){
warning("You asked for a strange initial value. We don't do that here. Switching to optimal.",
call.=FALSE);
initialType <- "o";
}
else{
# if(smoothType=="msarima" & initialValue=="o"){
# initialValue <- "b";
# warning(paste0("We don't support optimisation of the initial states of MSARIMA. ",
# "Switching to 'backcasting'."),
# call.=FALSE);
# }
initialType <- initialValue;
}
initialValue <- NULL;
}
else if(is.null(initialValue)){
if(silentText){
message("Initial value is not selected. Switching to optimal.");
}
initialType <- "o";
}
else if(!is.null(initialValue)){
if(!is.numeric(initialValue)){
warning(paste0("Initial vector is not numeric!\n",
"Values of initial vector will be estimated."),call.=FALSE);
initialValue <- NULL;
initialType <- "o";
}
else{
if(any(smoothType==c("es","oes"))){
if(modelDo!="estimate"){
warning(paste0("Predefined initials vector can only be used with preselected ETS model.\n",
"Changing to estimation of initials."),call.=FALSE);
initialValue <- NULL;
initialType <- "o";
}
else{
if(length(initialValue)>2){
warning(paste0("Length of initial vector is wrong! It should not be greater than 2.\n",
"Values of initial vector will be estimated."),call.=FALSE);
initialValue <- NULL;
initialType <- "o";
}
else{
if(length(initialValue) != (1*(Ttype!="N") + 1)){
warning(paste0("Length of initial vector is wrong! It should be ",
(1*(Ttype!="N") + 1),
" instead of ",length(initialValue),".\n",
"Values of initial vector will be estimated."),call.=FALSE);
initialValue <- NULL;
initialType <- "o";
}
else{
initialType <- "p";
# initialValue <- initial;
parametersNumber[2,1] <- parametersNumber[2,1] + length(initial);
}
}
}
}
else if(smoothType=="gum"){
if(length(initialValue) != (nComponents*max(lags))){
warning(paste0("Wrong length of initial vector. Should be ",(nComponents*max(lags)),
" instead of ",length(initial),".\n",
"Values of initial vector will be estimated."),call.=FALSE);
initialValue <- NULL;
initialType <- "o";
}
else{
initialType <- "p";
initialValue <- initial;
parametersNumber[2,1] <- parametersNumber[2,1] + orders %*% lags;
}
}
else if(smoothType=="ssarima"){
if(length(initialValue) != nComponents){
warning(paste0("Wrong length of initial vector. Should be ",nComponents,
" instead of ",length(initial),".\n",
"Values of initial vector will be estimated."),call.=FALSE);
initialValue <- NULL;
initialType <- "o";
}
else{
initialType <- "p";
initialValue <- initial;
parametersNumber[2,1] <- parametersNumber[2,1] + length(initial);
}
}
else if(smoothType=="msarima"){
if(length(initialValue) != nComponents*lagsModelMax){
warning(paste0("Wrong length of initial vector. Should be ",nComponents,
" instead of ",length(initial),".\n",
"Values of initial vector will be backcasted."),call.=FALSE);
initialValue <- NULL;
initialType <- "b";
}
else{
initialType <- "p";
# initialValue <- initial;
parametersNumber[2,1] <- parametersNumber[2,1] + length(initial);
}
}
else if(smoothType=="ces"){
if(length(initialValue) != lagsModelMax*nComponents){
warning(paste0("Wrong length of initial vector. Should be ",lagsModelMax*nComponents,
" instead of ",length(initial),".\n",
"Values of initial vector will be estimated."),call.=FALSE);
initialValue <- NULL;
initialType <- "o";
}
else{
initialType <- "p";
# initialValue <- initial;
parametersNumber[2,1] <- (parametersNumber[2,1] + 2*(seasonality!="s") +
lagsModelMax*(seasonality!="n") +
lagsModelMax*any(seasonality==c("f","s")));
}
}
else if(smoothType=="smoothC"){
warning("We cannot use the preset initials for the models. Switching to optimal.",
call.=FALSE);
initialType <- "o";
initialValue <- NULL;
}
}
}
if(any(smoothType==c("es","oes"))){
# If model selection is chosen, forget about the initial values and persistence
if(any(Etype=="Z",any(Ttype==c("X","Y","Z")),Stype=="Z")){
if(any(!is.null(initialValue),!is.null(initialSeason),!is.null(persistence),!is.null(phi))){
warning(paste0("Model selection doesn't go well with the predefined values.\n",
"Switching to estimation of all the parameters."),call.=FALSE);
initialValue <- NULL;
initialType <- "o";
initialSeason <- NULL;
persistence <- NULL;
phi <- NULL;
}
}
##### initialSeason for ES #####
if(!is.null(initialSeason)){
if(!is.numeric(initialSeason)){
warning(paste0("InitialSeason vector is not numeric!\n",
"Values of initialSeason vector will be estimated."),call.=FALSE);
initialSeason <- NULL;
initialSeasonEstimate <- TRUE;
}
else{
if(length(initialSeason)!=dataFreq){
warning(paste0("The length of initialSeason vector is wrong! ",
"It should correspond to the frequency of the data.\n",
"Values of initialSeason vector will be estimated."),call.=FALSE);
initialSeason <- NULL;
initialSeasonEstimate <- TRUE
}
else{
initialSeasonEstimate <- FALSE;
parametersNumber[2,1] <- parametersNumber[2,1] + length(initialSeason);
}
}
}
else{
initialSeasonEstimate <- TRUE;
}
# Check the length of the provided data. Say bad words if:
# 1. Seasonal model, <2 seasons of data and no initial seasonals.
# 2. Seasonal model, <1 season of data, no initial seasonals and no persistence.
if(is.null(modelsPool)){
if((modelIsSeasonal & (obsInSample < 2*dataFreq) & is.null(initialSeason)) |
(modelIsSeasonal & (obsInSample < dataFreq) & is.null(initialSeason) & is.null(persistence))){
if(is.null(initialSeason)){
warning(paste0("Sorry, but we don't have enough observations for the seasonal model!\n",
"Switching to non-seasonal."),call.=FALSE);
Stype <- "N";
modelIsSeasonal <- FALSE;
initialSeasonEstimate <- FALSE;
}
}
}
##### phi for ES #####
if(!is.null(phi)){
if(!is.numeric(phi) & (damped)){
warning(paste0("Provided value of phi is meaningless. phi will be estimated."),
call.=FALSE);
phi <- NULL;
phiEstimate <- TRUE;
}
else if(is.numeric(phi) & (phi<0 | phi>2)){
warning(paste0("Damping parameter should lie in (0, 2) region. ",
"Changing to the estimation of phi."),call.=FALSE);
phi <- NULL;
phiEstimate <- TRUE;
}
else{
phiEstimate <- FALSE;
if(damped){
parametersNumber[2,1] <- parametersNumber[2,1] + 1;
}
}
}
else{
if(damped){
phiEstimate <- TRUE;
}
else{
phiEstimate <- FALSE;
}
}
}
if(smoothType=="gum"){
##### transition for GUM #####
# Check the provided vector of initials: length and provided values.
if(!is.null(transition)){
if((!is.numeric(transition) | !is.vector(transition)) & !is.matrix(transition)){
warning(paste0("Transition matrix is not numeric!\n",
"The matrix will be estimated!"),call.=FALSE);
transitionEstimate <- TRUE;
}
else if(length(transition) != nComponents^2){
warning(paste0("Wrong length of transition matrix. Should be ",nComponents^2,
" instead of ",length(transition),".\n",
"The matrix will be estimated!"),call.=FALSE);
transitionEstimate <- TRUE;
}
else{
transitionEstimate <- FALSE;
parametersNumber[2,1] <- parametersNumber[2,1] + length(transition);
}
}
else{
transitionEstimate <- TRUE;
}
##### measurement for GUM #####
if(!is.null(measurement)){
if((!is.numeric(measurement) | !is.vector(measurement)) & !is.matrix(measurement)){
warning(paste0("Measurement vector is not numeric!\n",
"The vector will be estimated!"),call.=FALSE);
measurementEstimate <- TRUE;
}
else if(length(measurement) != nComponents){
warning(paste0("Wrong length of measurement vector. Should be ",nComponents,
" instead of ",length(measurement),".\n",
"The vector will be estimated!"),call.=FALSE);
measurementEstimate <- TRUE;
}
else{
measurementEstimate <- FALSE;
parametersNumber[2,1] <- parametersNumber[2,1] + length(measurement);
}
}
else{
measurementEstimate <- TRUE;
}
}
if(smoothType=="ssarima"){
if((nComponents==0) & (!constantRequired)){
if(!silentText){
warning("You have not defined any model! Constructing model with zero constant.",
call.=FALSE);
}
constantRequired <- TRUE;
constantValue <- 0;
initialType <- "p";
}
}
##### Calculate nParamMax for checks #####
if(any(smoothType==c("es","oes"))){
# 1: estimation of variance;
# 1 - 3: persitence vector;
# 1 - 2: initials;
# 1 - 1 phi value;
# dataFreq: dataFreq initials for seasonal component;
nParamMax <- (1 + (1 + (Ttype!="N") + (modelIsSeasonal))*persistenceEstimate +
(1 + (Ttype!="N"))*(initialType=="o") + phiEstimate*damped +
dataFreq*(modelIsSeasonal)*initialSeasonEstimate*(initialType!="b"));
}
else if(smoothType=="gum"){
nParamMax <- (1 + nComponents*measurementEstimate + nComponents*persistenceEstimate +
(nComponents^2)*transitionEstimate + (orders %*% lags)*(initialType=="o"));
}
else if(smoothType=="ssarima"){
nParamMax <- (1 + nComponents*(initialType=="o") + sum(ar.orders)*ARRequired*AREstimate +
sum(ma.orders)*MARequired*MAEstimate + constantRequired*constantEstimate);
}
else if(smoothType=="ces"){
nParamMax <- (1 + sum(lagsModel)*(initialType=="o") + a$number*a$estimate + b$number*b$estimate);
}
# Stop if number of observations is less than horizon and multisteps is chosen.
if((multisteps) & (obsNonzero < h+1) & all(loss!=c("aMSEh","aTMSE","aGTMSE","aGPL"))){
warning(paste0("Do you seriously think that you can use ",loss,
" with h=",h," on ",obsNonzero," non-zero observations?!"),call.=FALSE);
stop("Not enough observations for multisteps loss function.",call.=FALSE);
}
else if((multisteps) & (obsNonzero < 2*h) & all(loss!=c("aMSEh","aTMSE","aGTMSE","aGPL"))){
warning(paste0("Number of observations is really low for a multisteps loss function! ",
"We will, try but cannot guarantee anything..."),call.=FALSE);
}
normalizer <- mean(abs(diff(c(yInSample))));
##### Define regressors #####
if(smoothType!="sma"){
if(!any(regressors==c("use","select","u","s"))){
warning("Wrong type of regressors parameter. Changing to 'select'.", call.=FALSE);
regressors <- "select";
}
regressors <- substr(regressors[1],1,1);
}
if(is.null(xreg)){
regressors <- "u";
}
##### Fisher Information #####
if(!exists("FI",envir=ParentEnvironment,inherits=FALSE)){
FI <- FALSE;
}
else{
if(!is.logical(FI)){
FI <- FALSE;
}
if(!requireNamespace("numDeriv",quietly=TRUE) & FI){
warning(paste0("Sorry, but you don't have 'numDeriv' package, ",
"which is required in order to produce Fisher Information."),
call.=FALSE);
FI <- FALSE;
}
}
##### Rounded up values #####
if(!exists("rounded",envir=ParentEnvironment,inherits=FALSE)){
rounded <- FALSE;
}
else{
if(!is.logical(rounded)){
rounded <- FALSE;
}
else{
if(rounded){
loss <- "Rounded";
lossOriginal <- loss;
}
}
}
##### Return values to previous environment #####
assign("h",h,ParentEnvironment);
assign("holdout",holdout,ParentEnvironment);
assign("silentText",silentText,ParentEnvironment);
assign("silentGraph",silentGraph,ParentEnvironment);
assign("silentLegend",silentLegend,ParentEnvironment);
assign("obsInSample",obsInSample,ParentEnvironment);
assign("obsAll",obsAll,ParentEnvironment);
assign("obsStates",obsStates,ParentEnvironment);
assign("obsNonzero",obsNonzero,ParentEnvironment);
assign("obsZero",obsZero,ParentEnvironment);
assign("y",y,ParentEnvironment);
assign("yInSample",yInSample,ParentEnvironment);
assign("dataFreq",dataFreq,ParentEnvironment);
assign("dataStart",dataStart,ParentEnvironment);
assign("yForecastStart",yForecastStart,ParentEnvironment);
assign("bounds",bounds,ParentEnvironment);
assign("loss",loss,ParentEnvironment);
assign("lossOriginal",lossOriginal,ParentEnvironment);
assign("multisteps",multisteps,ParentEnvironment);
assign("intervalType",intervalType,ParentEnvironment);
assign("interval",interval,ParentEnvironment);
assign("initialValue",initialValue,ParentEnvironment);
assign("initialType",initialType,ParentEnvironment);
assign("normalizer",normalizer,ParentEnvironment);
assign("nParamMax",nParamMax,ParentEnvironment);
assign("regressors",regressors,ParentEnvironment);
assign("FI",FI,ParentEnvironment);
assign("rounded",rounded,ParentEnvironment);
assign("parametersNumber",parametersNumber,ParentEnvironment);
assign("ic",ic,ParentEnvironment);
#### intermittent part of the model... outdated ####
if(smoothType!="oes"){
assign("occurrence",occurrence,ParentEnvironment);
assign("occurrenceModel",occurrenceModel,ParentEnvironment);
assign("ot",ot,ParentEnvironment);
assign("yot",yot,ParentEnvironment);
assign("pFitted",pFitted,ParentEnvironment);
assign("pForecast",pForecast,ParentEnvironment);
assign("nParamOccurrence",nParamOccurrence,ParentEnvironment);
assign("occurrenceModelProvided",occurrenceModelProvided,ParentEnvironment);
}
else{
assign("initialSeasonEstimate",initialSeasonEstimate,ParentEnvironment);
assign("modelIsSeasonal",modelIsSeasonal,ParentEnvironment);
}
if(any(smoothType==c("es","oes"))){
assign("model",model,ParentEnvironment);
assign("modelsPool",modelsPool,ParentEnvironment);
assign("Etype",Etype,ParentEnvironment);
assign("Ttype",Ttype,ParentEnvironment);
assign("Stype",Stype,ParentEnvironment);
assign("damped",damped,ParentEnvironment);
assign("modelDo",modelDo,ParentEnvironment);
assign("initialSeason",initialSeason,ParentEnvironment);
assign("phi",phi,ParentEnvironment);
assign("phiEstimate",phiEstimate,ParentEnvironment);
if(smoothType=="es"){
assign("allowMultiplicative",allowMultiplicative,ParentEnvironment);
}
}
else if(smoothType=="gum"){
assign("transitionEstimate",transitionEstimate,ParentEnvironment);
assign("measurementEstimate",measurementEstimate,ParentEnvironment);
assign("orders",orders,ParentEnvironment);
assign("lags",lags,ParentEnvironment);
assign("modelIsMultiplicative",modelIsMultiplicative,ParentEnvironment);
}
else if(any(smoothType==c("ssarima","msarima"))){
assign("ar.orders",ar.orders,ParentEnvironment);
assign("i.orders",i.orders,ParentEnvironment);
assign("ma.orders",ma.orders,ParentEnvironment);
assign("lags",lags,ParentEnvironment);
assign("ARValue",ARValue,ParentEnvironment);
assign("ARRequired",ARRequired,ParentEnvironment);
assign("AREstimate",AREstimate,ParentEnvironment);
assign("MAValue",MAValue,ParentEnvironment);
assign("MARequired",MARequired,ParentEnvironment);
assign("MAEstimate",MAEstimate,ParentEnvironment);
assign("constantValue",constantValue,ParentEnvironment);
assign("constantEstimate",constantEstimate,ParentEnvironment);
assign("constantRequired",constantRequired,ParentEnvironment);
assign("nonZeroARI",nonZeroARI,ParentEnvironment);
assign("nonZeroMA",nonZeroMA,ParentEnvironment);
}
else if(smoothType=="ces"){
assign("seasonality",seasonality,ParentEnvironment);
assign("a",a,ParentEnvironment);
assign("b",b,ParentEnvironment);
}
if(any(smoothType==c("es","oes","gum"))){
assign("persistence",persistence,ParentEnvironment);
assign("persistenceEstimate",persistenceEstimate,ParentEnvironment);
}
if(any(smoothType==c("gum","ssarima","ces","msarima"))){
assign("nComponents",nComponents,ParentEnvironment);
assign("lagsModelMax",lagsModelMax,ParentEnvironment);
assign("lagsModel",lagsModel,ParentEnvironment);
}
}
##### *Checker for auto. functions* #####
ssAutoInput <- function(smoothType=c("auto.ces","auto.gum","auto.ssarima","auto.msarima"),...){
# This is universal function needed in order to check the passed arguments to auto.ces(),
# auto.gum() and auto.ssarima()
ellipsis <- list(...);
ParentEnvironment <- ellipsis[['ParentEnvironment']];
##### silent #####
silent <- silent[1];
# Fix for cases with TRUE/FALSE.
if(!is.logical(silent)){
if(all(silent!=c("none","all","graph","legend","output","debugging","n","a","g","l","o","d"))){
message(paste0("Sorry, I have no idea what 'silent=",silent,"' means. Switching to 'none'."));
silent <- "none";
}
silent <- substring(silent,1,1);
}
silentValue <- silent;
if(silentValue==FALSE | silentValue=="n"){
silentText <- FALSE;
silentGraph <- FALSE;
silentLegend <- FALSE;
}
else if(silentValue==TRUE | silentValue=="a"){
silentText <- TRUE;
silentGraph <- TRUE;
silentLegend <- TRUE;
}
else if(silentValue=="g"){
silentText <- FALSE;
silentGraph <- TRUE;
silentLegend <- TRUE;
}
else if(silentValue=="l"){
silentText <- FALSE;
silentGraph <- FALSE;
silentLegend <- TRUE;
}
else if(silentValue=="o"){
silentText <- TRUE;
silentGraph <- FALSE;
silentLegend <- FALSE;
}
else if(silentValue=="d"){
silentText <- TRUE;
silentGraph <- FALSE;
silentLegend <- FALSE;
}
# Check what was asked as a horizon
if(h<=0){
warning(paste0("You have set forecast horizon equal to ",h,". We hope you know, what you are doing."),
call.=FALSE);
if(h<0){
warning(paste0("And by the way, we can't do anything with negative horizon, ",
"so we will set it equal to zero."),
call.=FALSE);
h <- 0;
}
}
##### Fisher Information #####
if(!exists("FI",envir=ParentEnvironment,inherits=FALSE)){
FI <- FALSE;
}
##### data #####
if(any(is.smooth.sim(y))){
y <- y$data;
}
else if(any(class(y)=="Mdata")){
h <- y$h;
holdout <- TRUE;
y <- ts(c(y$x,y$xx),start=start(y$x),frequency=frequency(y$x));
}
if(!is.numeric(y)){
stop("The provided data is not a vector or ts object! Can't build any model!", call.=FALSE);
}
# Check the data for NAs
if(any(is.na(y))){
if(!silentText){
warning("Data contains NAs. These observations will be substituted by zeroes.",
call.=FALSE);
}
y[is.na(y)] <- 0;
}
##### Observations #####
# Define obs, the number of observations of in-sample
obsInSample <- length(y) - holdout*h;
# Define obsAll, the overal number of observations (in-sample + holdout)
obsAll <- length(y) + (1 - holdout)*h;
yInSample <- matrix(y[1:obsInSample],obsInSample,1);
dataFreq <- frequency(y);
dataStart <- start(y);
yForecastStart <- time(y)[obsInSample]+deltat(y);
# This is the critical minimum needed in order to at least fit ARIMA(0,0,0) with constant
if(obsInSample < 4){
stop("Sorry, but your sample is too small. Come back when you have at least 4 observations...",
call.=FALSE);
}
# Check the provided vector of initials: length and provided values.
initialValue <- initial;
if(is.character(initialValue)){
initialValue <- substring(initialValue[1],1,1);
if(initialValue!="o" & initialValue!="b"){
warning("You asked for a strange initial value. We don't do that here. Switching to optimal.",
call.=FALSE);
initialType <- "o";
}
else{
initialType <- initialValue;
}
initialValue <- NULL;
}
else if(is.null(initialValue)){
if(!silentText){
warning("Initial value is not selected. Switching to optimal.",call.=FALSE);
}
initialType <- "o";
}
else{
warning("Predefined initials don't go well with automatic model selection. Switching to optimal.",
call.=FALSE);
initialType <- "o";
initialValue <- NULL;
}
##### bounds #####
bounds <- substring(bounds[1],1,1);
# Check if "bounds" parameter makes any sense
if(all(bounds!=c("n","a","r"))){
warning("Strange bounds are defined. Switching to 'admissible'.",call.=FALSE);
bounds <- "a";
}
if(bounds=="n"){
warning("You have defined bounds='none'. ",
"This is dangerous and might lead to an unstable model or even break the function. ",
"Hopefully, you know what you are doing :).",
call.=FALSE);
}
##### Information Criteria #####
ic <- ic[1];
if(all(ic!=c("AICc","AIC","BIC","BICc"))){
warning(paste0("Strange type of information criteria defined: ",ic,". Switching to 'AICc'."),
call.=FALSE);
ic <- "AICc";
}
##### Loss function type #####
loss <- loss[1];
if(any(loss==c("MSEh","TMSE","GTMSE","MSCE","MAEh","TMAE","GTMAE","MACE",
"HAMh","THAM","GTHAM","CHAM",
"GPL","aMSEh","aTMSE","aGTMSE","aGPL"))){
multisteps <- TRUE;
}
else if(any(loss==c("likelihood","MSE","MAE","HAM","Rounded"))){
multisteps <- FALSE;
}
else{
warning(paste0("Strange loss function specified: ",loss,". Switching to 'MSE'."),
call.=FALSE);
loss <- "MSE";
multisteps <- FALSE;
}
if(!any(loss==c("likelihood","MSE","MAE","HAM","MSEh","MAEh","HAMh","MSCE","MACE","CHAM",
"GPL","aGPL"))){
warning(paste0("'",loss,"' is used as loss function instead of 'likelihood'. ",
"The results of the model selection may be wrong."),
call.=FALSE);
}
##### interval, intervalType, level #####
intervalType <- interval[1];
# Check the provided type of interval
if(is.logical(intervalType)){
if(intervalType){
intervalType <- "p";
}
else{
intervalType <- "none";
}
}
if(all(intervalType!=c("p","l","s","n","a","sp","np","none","parametric","likelihood","semiparametric","nonparametric"))){
warning(paste0("Wrong type of interval: '",intervalType, "'. Switching to 'parametric'."),call.=FALSE);
intervalType <- "p";
}
if(any(intervalType==c("none","n"))){
intervalType <- "n";
interval <- FALSE;
}
else if(any(intervalType==c("parametric","p"))){
intervalType <- "p";
interval <- TRUE;
}
else if(any(intervalType==c("semiparametric","sp"))){
intervalType <- "sp";
interval <- TRUE;
}
else if(any(intervalType==c("nonparametric","np"))){
intervalType <- "np";
interval <- TRUE;
}
else if(any(intervalType==c("likelihood","l"))){
intervalType <- "l";
interval <- TRUE;
}
else{
interval <- TRUE;
}
##### Occurrence part of the model #####
if(is.oes(occurrence)){
occurrenceModel <- occurrence;
occurrence <- occurrenceModel$occurrence;
occurrenceModelProvided <- TRUE;
}
else if(is.list(occurrence)){
warning(paste0("occurrence is not of the class oes. ",
"We will try to extract the type of model, but cannot promise anything."),
call.=FALSE);
occurrenceModel <- modelType(occurrence);
occurrence <- occurrenceModel$occurrence;
occurrenceModelProvided <- FALSE;
}
else if(is.null(occurrence)){
occurrence <- "none";
occurrenceModel <- "MNN";
occurrenceModelProvided <- FALSE;
}
else{
if(is.null(oesmodel) || is.na(oesmodel)){
occurrenceModel <- "MNN";
}
else{
occurrenceModel <- oesmodel;
}
occurrenceModelProvided <- FALSE;
}
if(exists("intermittent",envir=ParentEnvironment,inherits=FALSE)){
intermittent <- substr(intermittent[1],1,1);
warning("The parameter \"intermittent\" is obsolete. Please, use \"occurrence\" instead");
occurrence <- switch(intermittent,
"l"="o",
"p"="d",
"f"="f",
"n"="n",
"a"="a",
"i"=,
"s"="i");
}
if(exists("imodel",envir=ParentEnvironment,inherits=FALSE)){
if(!is.null(imodel)){
oesmodel <- imodel;
warning("The parameter \"imodel\" is obsolete. Please, use \"oesmodel\" instead");
}
else{
oesmodel <- imodel;
}
}
if(is.numeric(occurrence)){
obsNonzero <- sum((yInSample!=0)*1);
# If it is data, then it should either correspond to the whole sample (in-sample + holdout)
# or be equal to forecating horizon.
if(all(length(c(occurrence))!=c(h,obsAll))){
warning(paste0("Length of the provided future occurrences is ",length(c(occurrence)),
" while length of forecasting horizon is ",h,".\n",
"Where should we plug in the future occurences anyway?\n",
"Switching to occurrence='fixed'."),call.=FALSE);
occurrence <- "f";
}
if(any(occurrence!=0 & occurrence!=1)){
warning(paste0("Parameter 'occurrence' should contain only zeroes and ones.\n",
"Converting to appropriate vector."),call.=FALSE);
occurrence <- (occurrence!=0)*1;
}
}
else{
obsNonzero <- sum((yInSample!=0)*1);
occurrence <- occurrence[1];
if(all(occurrence!=c("n","a","f","g","o","i","d",
"none","auto","fixed","general","odds-ratio","inverse-odds-ratio","direct"))){
warning(paste0("Strange type of occurrence model defined: '",occurrence,"'. Switching to 'fixed'."),
call.=FALSE);
occurrence <- "f";
}
occurrence <- substring(occurrence,1,1);
environment(intermittentParametersSetter) <- environment();
intermittentParametersSetter(occurrence,ParentEnvironment=environment());
}
# If the data is not occurrence, let's assume that the parameter was switched unintentionally.
if(all(ot==1) & all(occurrence!=c("n","p","provided"))){
occurrence <- "n";
occurrenceModelProvided <- FALSE;
}
##### Define regressors #####
if(!any(regressors==c("use","select","u","s"))){
warning("Wrong type of regressors parameter. Changing to 'select'.", call.=FALSE);
regressors <- "select";
}
regressors <- substr(regressors[1],1,1);
if(is.null(xreg)){
regressors <- "u";
}
##### Return values to previous environment #####
assign("h",h,ParentEnvironment);
assign("holdout",holdout,ParentEnvironment);
assign("silentText",silentText,ParentEnvironment);
assign("silentGraph",silentGraph,ParentEnvironment);
assign("silentLegend",silentLegend,ParentEnvironment);
assign("bounds",bounds,ParentEnvironment);
assign("FI",FI,ParentEnvironment);
assign("obsInSample",obsInSample,ParentEnvironment);
assign("obsAll",obsAll,ParentEnvironment);
assign("obsNonzero",obsNonzero,ParentEnvironment);
assign("initialValue",initialValue,ParentEnvironment);
assign("initialType",initialType,ParentEnvironment);
assign("ic",ic,ParentEnvironment);
assign("loss",loss,ParentEnvironment);
assign("multisteps",multisteps,ParentEnvironment);
assign("interval",interval,ParentEnvironment);
assign("intervalType",intervalType,ParentEnvironment);
assign("occurrence",occurrence,ParentEnvironment);
assign("yInSample",yInSample,ParentEnvironment);
assign("y",y,ParentEnvironment);
assign("dataFreq",dataFreq,ParentEnvironment);
assign("dataStart",dataStart,ParentEnvironment);
assign("yForecastStart",yForecastStart,ParentEnvironment);
assign("regressors",regressors,ParentEnvironment);
}
##### *ssFitter function* #####
ssFitter <- function(...){
ellipsis <- list(...);
ParentEnvironment <- ellipsis[['ParentEnvironment']];
fitting <- fitterwrap(matvt, matF, matw, yInSample, vecg,
lagsModel, Etype, Ttype, Stype, initialType,
matxt, matat, matFX, vecgX, ot);
statesNames <- colnames(matvt);
matvt <- ts(fitting$matvt,start=(time(y)[1] - deltat(y)*lagsModelMax),frequency=dataFreq);
colnames(matvt) <- statesNames;
yFitted <- ts(fitting$yfit,start=dataStart,frequency=dataFreq);
errors <- ts(fitting$errors,start=dataStart,frequency=dataFreq);
if(any(is.nan(matvt[,1]))){
matvt[is.nan(matvt[,1]),1] <- 0;
warning(paste0("Something went wrong with the model ",model,", NaNs were produced.\n",
"This could happen if you used multiplicative model on non-positive data. ",
"Please, use a different model."),
call.=FALSE);
}
if(Etype=="M" & any(matvt[,1]<0)){
matvt[matvt[,1]<0,1] <- 0.001;
warning(paste0("Negative values produced in the level of state vector of model ",model,".\n",
"We had to substitute them by low values. Please, use a different model."),
call.=FALSE);
}
if(!is.null(xreg)){
# Write down the matat and copy values for the holdout
matat[1:nrow(fitting$matat),] <- fitting$matat;
}
if(interval && h>0){
errors.mat <- ts(errorerwrap(matvt, matF, matw, yInSample,
h, Etype, Ttype, Stype, lagsModel,
matxt, matat, matFX, ot),
start=dataStart,frequency=dataFreq);
colnames(errors.mat) <- paste0("Error",c(1:h));
}
else{
errors.mat <- NA;
}
# Correct the fitted values for the cases of intermittent models.
yFitted[] <- yFitted * pFitted;
assign("matvt",matvt,ParentEnvironment);
assign("yFitted",yFitted,ParentEnvironment);
assign("matat",matat,ParentEnvironment);
assign("errors.mat",errors.mat,ParentEnvironment);
assign("errors",errors,ParentEnvironment);
}
##### *State space interval* #####
ssIntervals <- function(errors, ev=median(errors), level=0.95, intervalType=c("a","p","l","sp","np"), df=NULL,
measurement=NULL, transition=NULL, persistence=NULL, s2=NULL,
lagsModel=NULL, states=NULL, cumulative=FALSE, loss="MSE",
yForecast=rep(0,ncol(errors)), Etype="A", Ttype="N", Stype="N", s2g=NULL,
probability=1){
# Function constructs interval based on the provided random variable.
# If errors is a matrix, then it is assumed that each column has a variable that needs an interval.
# based on errors the horison is estimated as ncol(errors)
# Make a correction for the cases, when likelihood is used for the estimation
if(loss=="likelihood"){
loss <- "MSE";
}
matrixpower <- function(A,n){
if(n==0){
return(diag(nrow(A)));
}
else if(n==1){
return(A);
}
else if(n>1){
return(A %*% matrixpower(A, n-1));
}
}
hsmN <- gamma(0.75)*pi^(-0.5)*2^(-0.75);
intervalType <- intervalType[1]
# Check the provided type of interval
if(is.logical(intervalType)){
if(intervalType){
intervalType <- "p";
}
else{
intervalType <- "none";
}
}
if(all(intervalType!=c("a","p","l","s","n","a","sp","np","none","parametric","likelihood",
"semiparametric","nonparametric","asymmetric"))){
stop(paste0("What do you mean by 'intervalType=",intervalType,"'? I can't work with this!"),
call.=FALSE);
}
if(intervalType=="none"){
intervalType <- "n";
}
else if(intervalType=="parametric"){
intervalType <- "p";
}
# If it is likelihood, then it is "parametric", just uses a differen number of degrees of freedom
else if(any(intervalType==c("likelihood","l"))){
intervalType <- "p";
}
else if(intervalType=="semiparametric"){
intervalType <- "sp";
}
else if(intervalType=="nonparametric"){
intervalType <- "np";
}
if(intervalType=="p"){
if(any(is.null(measurement),is.null(transition),is.null(persistence),is.null(s2),is.null(lagsModel))){
stop(paste0("measurement, transition, persistence, s2 and lagsModel ",
"need to be provided in order to construct the parametric interval!"),
call.=FALSE);
}
if(any(!is.matrix(measurement),!is.matrix(transition),!is.matrix(persistence))){
stop(paste0("measurement, transition and persistence must me matrices. ",
"Can't do stuff with what you've provided."),
call.=FALSE);
}
}
# Function allows to estimate the coefficients of the simple quantile regression.
# Used in interval construction.
quantfunc <- function(A){
ee[] <- ye - (A[1]*xe^A[2]);
return((1-quant)*sum(abs(ee[ee<0]))+quant*sum(abs(ee[ee>=0])));
}
# Function returns quantiles of Bernoulli-lognormal cumulative distribution for a predefined parameters
qlnormBin <- function(probability, level=0.95, meanVec=0, sdVec=1, Etype="A"){
levelResidual <- (level - (1-probability)) / probability
lowerquant <- upperquant <- rep(0,length(sdVec));
positiveLevels <- levelResidual>0;
if(any(positiveLevels)){
# If this is Laplace or S, then get b values
if(loss=="MAE"){
sdVec <- sqrt(sdVec/2);
}
else if(loss=="HAM"){
sdVec <- (sdVec/120)^0.25;
}
# Produce lower quantiles if the probability is still lower than the lower P
if(Etype=="A" | all(Etype=="M",all((1-probability) < (1-level)/2))){
if(Etype=="M"){
if(loss=="MAE"){
lowerquant[positiveLevels] <- exp(qlaplace((1-levelResidual[positiveLevels])/2,
meanVec,sdVec));
}
else if(loss=="HAM"){
lowerquant[positiveLevels] <- exp(qs((1-levelResidual[positiveLevels])/2,
meanVec,sdVec));
}
else{
lowerquant[positiveLevels] <- qlnorm((1-levelResidual[positiveLevels])/2,
meanVec,sdVec);
}
}
else{
if(loss=="MAE"){
lowerquant[positiveLevels] <- qlaplace((1-levelResidual[positiveLevels])/2,
meanVec,sdVec);
}
else if(loss=="HAM"){
lowerquant[positiveLevels] <- qs((1-levelResidual[positiveLevels])/2,
meanVec,sdVec);
}
else{
lowerquant[positiveLevels] <- qnorm((1-levelResidual[positiveLevels])/2,
meanVec,sdVec);
}
}
levelNew <- (1+levelResidual[positiveLevels])/2;
}
else{
levelNew <- levelResidual[positiveLevels];
}
# Produce upper quantiles
if(Etype=="M"){
if(loss=="MAE"){
upperquant[positiveLevels] <- exp(qlaplace(levelNew,meanVec,sdVec));
}
else if(loss=="HAM"){
upperquant[positiveLevels] <- exp(qs(levelNew,meanVec,sdVec));
}
else{
upperquant[positiveLevels] <- qlnorm(levelNew,meanVec,sdVec);
}
}
else{
if(loss=="MAE"){
upperquant[positiveLevels] <- qlaplace(levelNew,meanVec,sdVec);
}
else if(loss=="HAM"){
upperquant[positiveLevels] <- qs(levelNew,meanVec,sdVec);
}
else{
upperquant[positiveLevels] <- qnorm(levelNew,meanVec,sdVec);
}
}
}
return(list(lower=lowerquant,upper=upperquant));
}
if(loss=="MAE"){
upperquant <- qlaplace((1+level)/2,0,1);
lowerquant <- qlaplace((1-level)/2,0,1);
}
else if(loss=="HAM"){
upperquant <- qs((1+level)/2,0,1);
lowerquant <- qs((1-level)/2,0,1);
}
else{
#if(loss=="MSE")
# If degrees of freedom are provided, use Student's distribution. Otherwise stick with normal.
if(is.null(df)){
upperquant <- qnorm((1+level)/2,0,1);
lowerquant <- qnorm((1-level)/2,0,1);
}
else{
if(df>0){
upperquant <- qt((1+level)/2,df=df);
lowerquant <- qt((1-level)/2,df=df);
}
else{
upperquant <- sqrt(1/((1-level)/2));
lowerquant <- -upperquant;
}
}
}
##### If they want us to produce several steps ahead #####
if(is.matrix(errors) | is.data.frame(errors)){
if(!cumulative){
nVariables <- ncol(errors);
}
else{
nVariables <- 1;
}
obs <- nrow(errors);
if(length(ev)==1){
ev <- rep(ev,nVariables);
}
upper <- rep(NA,nVariables);
lower <- rep(NA,nVariables);
#### Asymmetric interval using HM ####
if(intervalType=="a"){
if(!cumulative){
for(i in 1:nVariables){
upper[i] <- ev[i] + upperquant / hsmN^2 * Re(hm(errors[,i],ev[i]))^2;
lower[i] <- ev[i] + lowerquant / hsmN^2 * Im(hm(errors[,i],ev[i]))^2;
}
}
else{
upper <- ev + upperquant / hsmN^2 * Re(hm(rowSums(errors),sum(ev)))^2;
lower <- ev + lowerquant / hsmN^2 * Im(hm(rowSums(errors),sum(ev)))^2;
}
if(Etype=="M"){
upper <- yForecast*(1 + upper);
lower <- yForecast*(1 + lower);
}
varVec <- NULL;
}
#### Semiparametric interval using the variance of errors ####
else if(intervalType=="sp"){
if(Etype=="M"){
errors[errors < -1] <- -0.999;
if(!cumulative){
varVec <- colSums(log(1+errors)^2,na.rm=T)/df;
if(any(probability!=1)){
quants <- qlnormBin(probability, level=level, meanVec=log(yForecast),
sdVec=sqrt(varVec), Etype="M");
upper <- quants$upper;
lower <- quants$lower;
}
else{
if(loss=="MAE"){
varVec <- sqrt(varVec/2);
upper <- exp(qlaplace((1+level)/2,0,varVec));
lower <- exp(qlaplace((1-level)/2,0,varVec));
}
else if(loss=="HAM"){
varVec <- (varVec/120)^0.25;
upper <- exp(qs((1+level)/2,0,varVec));
lower <- exp(qs((1-level)/2,0,varVec));
}
else{
upper <- qlnorm((1+level)/2,rep(0,nVariables),sqrt(varVec));
lower <- qlnorm((1-level)/2,rep(0,nVariables),sqrt(varVec));
}
}
upper <- yForecast*upper;
lower <- yForecast*lower;
}
else{
#This is wrong. And there's not way to make it right.
varVec <- sum(rowSums(log(1+errors))^2,na.rm=T)/df;
if(any(probability!=1)){
quants <- qlnormBin(probability, level=level, meanVec=log(sum(yForecast)),
sdVec=sqrt(varVec), Etype="M");
upper <- quants$upper;
lower <- quants$lower;
}
else{
if(loss=="MAE"){
varVec <- sqrt(varVec/2);
upper <- exp(qlaplace((1+level)/2,0,varVec));
lower <- exp(qlaplace((1-level)/2,0,varVec));
}
else if(loss=="HAM"){
varVec <- (varVec/120)^0.25;
upper <- exp(qs((1+level)/2,0,varVec));
lower <- exp(qs((1-level)/2,0,varVec));
}
else{
upper <- qlnorm((1+level)/2,rep(0,nVariables),sqrt(varVec));
lower <- qlnorm((1-level)/2,rep(0,nVariables),sqrt(varVec));
}
}
upper <- sum(yForecast)*upper;
lower <- sum(yForecast)*lower;
}
}
else{
if(!cumulative){
errors <- errors - matrix(ev,nrow=obs,ncol=nVariables,byrow=T);
varVec <- colSums(errors^2,na.rm=T)/df;
if(any(probability!=1)){
quants <- qlnormBin(probability, level=level, meanVec=ev, sdVec=sqrt(varVec), Etype="A");
upper <- ev + quants$upper;
lower <- ev + quants$lower;
}
else{
if(loss=="MAE"){
# s^2 = 2 b^2 => b^2 = s^2 / 2
varVec <- varVec / 2;
}
else if(loss=="HAM"){
# s^2 = 120 b^4 => b^4 = s^2 / 120
# S(mu, b) = S(mu, 1) * 50^2
varVec <- varVec/120;
}
upper <- ev + upperquant * sqrt(varVec);
lower <- ev + lowerquant * sqrt(varVec);
}
}
else{
errors <- errors - matrix(ev,nrow=obs,ncol=ncol(errors),byrow=T);
varVec <- sum(rowSums(errors,na.rm=T)^2,na.rm=T)/df;
if(any(probability!=1)){
quants <- qlnormBin(probability, level=level, meanVec=sum(ev), sdVec=sqrt(varVec), Etype="A");
upper <- sum(ev) + quants$upper;
lower <- sum(ev) + quants$lower;
}
else{
if(loss=="MAE"){
# s^2 = 2 b^2 => b^2 = s^2 / 2
varVec <- varVec / 2;
}
else if(loss=="HAM"){
# s^2 = 120 b^4 => b^4 = s^2 / 120
# S(mu, b) = S(mu, 1) * 50^2
varVec <- varVec/120;
}
upper <- sum(ev) + upperquant * sqrt(varVec);
lower <- sum(ev) + lowerquant * sqrt(varVec);
}
}
}
}
#### Nonparametric interval using Taylor and Bunn, 1999 ####
else if(intervalType=="np"){
nonNAobs <- apply(!is.na(errors),1,all);
ye <- errors[nonNAobs,];
if(Etype=="M"){
ye <- 1 + ye;
}
# Define the correct bounds for the intermittent model
levelResidual <- (level - (1-probability)) / probability;
lower <- upper <- rep(0,length(yForecast));
if(!cumulative){
ee <- ye;
xe <- matrix(c(1:nVariables),nrow=sum(nonNAobs),ncol=nVariables,byrow=TRUE);
if(Etype=="A" | all(Etype=="M",all((1-probability) < (1-level)/2))){
A <- rep(1,2);
quant <- (1-levelResidual)/2;
A <- nlminb(A,quantfunc)$par;
lower <- A[1]*c(1:nVariables)^A[2];
levelNew <- (1+levelResidual)/2;
}
else{
levelNew <- levelResidual;
}
A <- rep(1,2);
quant <- levelNew;
A <- nlminb(A,quantfunc)$par;
upper <- A[1]*c(1:nVariables)^A[2];
if(Etype=="M"){
upper <- yForecast * upper;
lower <- yForecast * lower;
}
}
else{
if(Etype=="A" | all(Etype=="M",all((1-probability) < (1-level)/2))){
#This is wrong. And there's no way to make it right.
lower <- quantile(rowSums(ye),(1-levelResidual)/2);
levelNew <- (1+levelResidual)/2;
}
else{
levelNew <- levelResidual;
}
upper <- quantile(rowSums(ye),levelNew);
}
varVec <- NULL;
}
#### Parametric interval ####
else if(intervalType=="p"){
h <- length(yForecast);
# Vector of final variances
varVec <- rep(NA,h);
#### Pure Multiplicative models ####
if(Etype=="M"){
# This is just an approximation of the true interval
covarMat <- covarAnal(lagsModel, h, measurement, transition, persistence, s2);
### Cumulative variance is different.
if(cumulative){
varVec <- sum(covarMat);
varVec <- log(exp(varVec / h) * h);
if(any(probability!=1)){
quants <- qlnormBin(probability, level=level, meanVec=log(sum(yForecast)),
sdVec=sqrt(varVec), Etype="M");
upper <- quants$upper;
lower <- quants$lower;
}
else{
if(loss=="MAE"){
varVec <- sqrt(varVec / 2);
upper <- exp(qlaplace((1+level)/2,0,varVec));
lower <- exp(qlaplace((1-level)/2,0,varVec));
}
else if(loss=="HAM"){
varVec <- (varVec/120)^0.25;
upper <- exp(qs((1+level)/2,0,varVec));
lower <- exp(qs((1-level)/2,0,varVec));
}
else{
# Produce quantiles for log-normal dist with the specified variance
upper <- qlnorm((1+level)/2,0,sqrt(varVec));
lower <- qlnorm((1-level)/2,0,sqrt(varVec));
}
upper <- sum(yForecast)*upper;
lower <- sum(yForecast)*lower;
}
}
else{
varVec <- diag(covarMat);
if(any(probability!=1)){
quants <- qlnormBin(probability, level=level, meanVec=log(yForecast),
sdVec=sqrt(varVec), Etype="M");
upper <- quants$upper;
lower <- quants$lower;
}
else{
if(loss=="MAE"){
# s^2 = 2 b^2 => b = sqrt(s^2 / 2)
varVec <- sqrt(varVec / 2);
upper <- exp(qlaplace((1+level)/2,0,varVec));
lower <- exp(qlaplace((1-level)/2,0,varVec));
}
else if(loss=="HAM"){
# s^2 = 120 b^4 => b^4 = s^2 / 120
# S(mu, b) = S(mu, 1) * 50^2
varVec <- (varVec/120)^0.25;
upper <- exp(qs((1+level)/2,0,varVec));
lower <- exp(qs((1-level)/2,0,varVec));
}
else{
# Produce quantiles for log-normal dist with the specified variance
upper <- qlnorm((1+level)/2,0,sqrt(varVec));
lower <- qlnorm((1-level)/2,0,sqrt(varVec));
}
upper <- yForecast*upper;
lower <- yForecast*lower;
}
}
}
#### Multiplicative error and additive trend / seasonality
# else if(Etype=="M" & all(c(Ttype,Stype)!="M") & all(c(Ttype,Stype)!="N")){
# }
#### Pure Additive models ####
else{
covarMat <- covarAnal(lagsModel, h, measurement, transition, persistence, s2);
### Cumulative variance is a sum of all the elements of the matrix
if(cumulative){
varVec <- sum(covarMat);
}
else{
varVec <- diag(covarMat);
}
if(any(probability!=1)){
# Take intermittent data into account
quants <- qlnormBin(probability, level=level, meanVec=rep(0,length(varVec)),
sdVec=sqrt(varVec), Etype="A");
upper <- quants$upper;
lower <- quants$lower;
}
else{
if(loss=="MAE"){
# s^2 = 2 b^2 => b^2 = s^2 / 2
varVec <- varVec / 2;
}
else if(loss=="HAM"){
# s^2 = 120 b^4 => b^4 = s^2 / 120
# S(mu, b) = S(mu, 1) * b^2
varVec <- varVec/120;
}
upper <- upperquant * sqrt(varVec);
lower <- lowerquant * sqrt(varVec);
}
}
}
}
##### If we were asked to produce 1 value #####
else if(is.numeric(errors) & length(errors)>1 & !is.array(errors)){
if(length(ev)>1){
stop("Provided expected value doesn't correspond to the dimension of errors.", call.=FALSE);
}
if(intervalType=="a"){
upper <- ev + upperquant / hsmN^2 * Re(hm(errors,ev))^2;
lower <- ev + lowerquant / hsmN^2 * Im(hm(errors,ev))^2;
}
else if(any(intervalType==c("sp","p"))){
if(Etype=="M"){
if(any(probability!=1)){
quants <- qlnormBin(probability, level=level, meanVec=0, sdVec=sqrt(s2), Etype="M");
upper <- quants$upper;
lower <- quants$lower;
}
else{
if(loss=="MAE"){
# s^2 = 2 b^2 => b = sqrt(s^2 / 2)
s2 <- sqrt(s2 / 2);
upper <- exp(qlaplace((1+level)/2,0,s2));
lower <- exp(qlaplace((1-level)/2,0,s2));
}
else if(loss=="HAM"){
# s^2 = 120 b^4 => b^4 = s^2 / 120
# S(mu, b) = S(mu, 1) * 50^2
s2 <- (s2/120)^0.25;
upper <- exp(qs((1+level)/2,0,s2));
lower <- exp(qs((1-level)/2,0,s2));
}
else{
upper <- qlnorm((1+level)/2,0,sqrt(s2));
lower <- qlnorm((1-level)/2,0,sqrt(s2));
}
upper <- yForecast*upper;
lower <- yForecast*lower;
}
}
else{
if(any(probability!=1)){
quants <- qlnormBin(probability, level=level, meanVec=ev, sdVec=sqrt(s2), Etype="A");
upper <- quants$upper;
lower <- quants$lower;
}
else{
if(loss=="MAE"){
# s^2 = 2 b^2 => b^2 = s^2 / 2
s2 <- s2 / 2;
}
else if(loss=="HAM"){
# s^2 = 120 b^4 => b^4 = s^2 / 120
# S(mu, b) = S(mu, 1) * 50^2
s2 <- s2/120;
}
upper <- ev + upperquant * sqrt(s2);
lower <- ev + lowerquant * sqrt(s2);
}
}
}
else if(intervalType=="np"){
if(Etype=="M"){
errors <- errors + 1;
}
upper <- quantile(errors,(1+level)/2);
lower <- quantile(errors,(1-level)/2);
}
varVec <- NULL;
}
else{
stop("The provided data is not either vector or matrix. Can't do anything with it!", call.=FALSE);
}
return(list(upper=upper,lower=lower));
}
##### *Forecaster of state space functions* #####
ssForecaster <- function(...){
ellipsis <- list(...);
ParentEnvironment <- ellipsis[['ParentEnvironment']];
if(intervalType!="l" && obsInSample > nParam){
obsDF <- obsInSample - nParam;
}
else{
obsDF <- obsInSample;
}
if(!rounded){
s2 <- as.vector(sum((errors*ot)^2)/obsDF);
# If error is additive, s2g is just 1
if(Etype=="A"){
s2g <- 1;
}
else{
s2g <- log(1 + vecg %*% as.vector(errors*ot)) %*% t(log(1 + vecg %*%
as.vector(errors*ot)))/obsDF;
}
}
if(h>0){
yForecast <- ts(c(forecasterwrap(matvt[(obsInSample+1):(obsInSample+lagsModelMax),,drop=FALSE],
matF, matw, h, Etype, Ttype, Stype, lagsModel,
matxt[(obsAll-h+1):(obsAll),,drop=FALSE],
matat[(obsAll-h+1):(obsAll),,drop=FALSE], matFX)),
start=yForecastStart,frequency=dataFreq);
if(any(is.nan(yForecast))){
warning(paste0("NaNs were produced in the forecast.\n",
"This could happen if you used multiplicative model on non-positive data. ",
"Please, use a different model."),
call.=FALSE);
yForecast[] <- 0;
}
if(Etype=="M" & any(yForecast<0)){
warning(paste0("Negative values produced in forecast. ",
"This does not make any sense for model with multiplicative error.\n",
"Please, use another model."),
call.=FALSE);
if(interval){
warning("And don't expect anything reasonable from the prediction interval!",
call.=FALSE);
}
}
# Write down the forecasting interval
if(interval){
if(h==1){
errors.x <- as.vector(errors);
ev <- median(errors);
}
else{
errors.x <- errors.mat;
ev <- apply(errors.mat,2,median,na.rm=TRUE);
}
if(intervalType!="a"){
ev <- 0;
}
# We don't simulate pure additive models, pure multiplicative and
# additive models with multiplicative error,
# because they can be approximated by the pure additive ones
if(any(intervalType==c("p","l"))){
if(all(c(Etype,Stype,Ttype)!="M") |
all(c(Etype,Stype,Ttype)!="A") |
(all(Etype=="M",any(Ttype==c("A","N")),any(Stype==c("A","N"))) & s2<0.1)){
simulateIntervals <- FALSE;
}
else{
simulateIntervals <- TRUE;
}
}
else{
simulateIntervals <- FALSE;
}
# It is not possible to produce parametric / semi / non interval for cumulative values
# of multiplicative model. So we use simulations instead.
# if(Etype=="M"){
# simulateIntervals <- TRUE;
# }
if(simulateIntervals){
nSamples <- 100000;
matg <- matrix(vecg,nComponents,nSamples);
arrvt <- array(NA,c(h+lagsModelMax,nComponents,nSamples));
arrvt[1:lagsModelMax,,] <- rep(matvt[obsInSample+(1:lagsModelMax),],nSamples);
materrors <- matrix(rnorm(h*nSamples,0,sqrt(s2)),h,nSamples);
if(Etype=="M"){
materrors <- exp(materrors) - 1;
}
if(all(occurrence!=c("n","p"))){
matot <- matrix(rbinom(h*nSamples,1,pForecast),h,nSamples);
}
else{
matot <- matrix(1,h,nSamples);
}
ySimulated <- simulatorwrap(arrvt,materrors,matot,array(matF,c(dim(matF),nSamples)),matw,matg,
Etype,Ttype,Stype,lagsModel)$matyt;
if(!is.null(xreg)){
yForecastExo <- (c(yForecast) -
forecasterwrap(matrix(matvt[(obsInSample+1):(obsInSample+lagsModelMax),],
nrow=lagsModelMax),
matF, matw, h, Etype, Ttype, Stype, lagsModel,
matrix(rep(1,h),ncol=1), matrix(rep(0,h),ncol=1),
matrix(1,1,1)));
}
else{
yForecastExo <- rep(0,h);
}
if(Etype=="M"){
yForecast[] <- apply(ySimulated, 1, mean);
}
if(rounded){
ySimulated <- ceiling(ySimulated + matrix(yForecastExo,nrow=h,ncol=nSamples));
quantileType <- 1;
for(i in 1:h){
yForecast[i] <- median(ySimulated[i,ySimulated[i,]!=0]);
}
# NA means that there were no non-zero demands
yForecast[is.na(yForecast)] <- 0;
}
else{
ySimulated <- ySimulated + matrix(yForecastExo,nrow=h,ncol=nSamples);
quantileType <- 7;
}
yForecast[] <- yForecast + yForecastExo;
yForecast[] <- pForecast * yForecast;
if(cumulative){
yForecast <- ts(sum(yForecast),start=yForecastStart,frequency=dataFreq);
yLower <- ts(quantile(colSums(ySimulated,na.rm=T),(1-level)/2,type=quantileType),
start=yForecastStart,frequency=dataFreq);
yUpper <- ts(quantile(colSums(ySimulated,na.rm=T),(1+level)/2,type=quantileType),
start=yForecastStart,frequency=dataFreq);
}
else{
# yForecast <- ts(yForecast,start=yForecastStart,frequency=dataFreq);
yLower <- ts(apply(ySimulated,1,quantile,(1-level)/2,na.rm=T,type=quantileType) +
yForecastExo,
start=yForecastStart,frequency=dataFreq);
yUpper <- ts(apply(ySimulated,1,quantile,(1+level)/2,na.rm=T,type=quantileType) +
yForecastExo,
start=yForecastStart,frequency=dataFreq);
}
}
else{
quantvalues <- ssIntervals(errors.x, ev=ev, level=level, intervalType=intervalType,
df=obsDF,
measurement=matw, transition=matF, persistence=vecg, s2=s2,
lagsModel=lagsModel, states=matvt[(obsInSample-lagsModelMax+1):obsInSample,],
cumulative=cumulative, loss=loss,
yForecast=yForecast, Etype=Etype, Ttype=Ttype, Stype=Stype, s2g=s2g,
probability=pForecast);
# if(!(intervalType=="sp" & Etype=="M")){
yForecast[] <- c(pForecast) * c(yForecast);
# }
if(cumulative){
yForecast <- ts(sum(yForecast),start=yForecastStart,frequency=dataFreq);
}
if(Etype=="A"){
yLower <- ts(c(yForecast) + quantvalues$lower,start=yForecastStart,frequency=dataFreq);
yUpper <- ts(c(yForecast) + quantvalues$upper,start=yForecastStart,frequency=dataFreq);
}
else{
# if(any(intervalType==c("np","sp","a"))){
# quantvalues$upper <- quantvalues$upper * yForecast;
# quantvalues$lower <- quantvalues$lower * yForecast;
# }
yLower <- ts(quantvalues$lower,start=yForecastStart,frequency=dataFreq);
yUpper <- ts(quantvalues$upper,start=yForecastStart,frequency=dataFreq);
}
if(rounded){
yLower <- ceiling(yLower);
yUpper <- ceiling(yUpper);
}
}
}
else{
yLower <- NA;
yUpper <- NA;
if(rounded){
yForecast[] <- ceiling(yForecast);
}
yForecast[] <- pForecast*yForecast;
if(cumulative){
yForecast <- ts(sum(yForecast),start=yForecastStart,frequency=dataFreq);
}
# else{
# yForecast <- ts(yForecast,start=yForecastStart,frequency=dataFreq);
# }
}
}
else{
yLower <- NA;
yUpper <- NA;
yForecast <- ts(NA,start=yForecastStart,frequency=dataFreq);
}
if(any(is.na(yFitted),all(is.na(yForecast),h>0))){
warning("Something went wrong during the optimisation and NAs were produced!",call.=FALSE);
warning("Please check the input and report this error to the maintainer if it persists.",call.=FALSE);
}
assign("s2",s2,ParentEnvironment);
assign("yForecast",yForecast,ParentEnvironment);
assign("yLower",yLower,ParentEnvironment);
assign("yUpper",yUpper,ParentEnvironment);
}
##### *Check and initialisation of xreg* #####
ssXreg <- function(y, Etype="A", xreg=NULL, updateX=FALSE, ot=NULL,
persistenceX=NULL, transitionX=NULL, initialX=NULL,
obsInSample, obsAll, obsStates, lagsModelMax=1, h=1, regressors="u", silent=FALSE,
allowMultiplicative=FALSE){
# The function does general checks needed for exogenouse variables and returns the list of
# necessary parameters
if(!is.null(xreg)){
xreg <- as.matrix(xreg);
if(any(is.na(xreg))){
warning(paste0("The exogenous variables contain NAs! ",
"This may lead to problems during estimation and in forecasting.",
"\nSubstituting them with 0."),
call.=FALSE);
xreg[is.na(xreg)] <- 0;
}
if(!is.null(dim(xreg))){
if(ncol(xreg)==1){
xreg <- as.vector(xreg);
}
if(length(dim(xreg))>2){
stop(paste0("Sorry, but we don't deal with multidimensional arrays as exogenous variables here.\n",
"Pleas think of your behaviour and come back with matrix!"),call.=FALSE);
}
}
##### The case with vectors and ts objects, but not matrices
if(is.vector(xreg) | (is.ts(xreg) & !is.matrix(xreg))){
# Check if xreg contains something meaningful
if(is.null(initialX)){
if(all(xreg[1:obsInSample]==xreg[1])){
warning(paste0("The exogenous variable has no variability. ",
"Cannot do anything with that, so dropping out xreg."),
call.=FALSE);
xreg <- NULL;
}
}
if(!is.null(xreg)){
if(length(xreg) < obsAll){
warning("xreg did not contain values for the holdout, so we had to predict missing values.",
call.=FALSE);
# If this is a binary variable, use iss function.
if(all((xreg==0) | (xreg==1))){
xregForecast <- oes(xreg, model="MNN", h=obsAll-length(xreg),
occurrence="o", ic="AIC")$forecast;
}
else{
xregForecast <- es(xreg, h=obsAll-length(xreg), ic="AICc",silent=TRUE)$forecast;
}
xreg <- c(as.vector(xreg),as.vector(xregForecast));
}
else if(length(xreg) > obsAll){
warning("xreg contained too many observations, so we had to cut off some of them.",
call.=FALSE);
xreg <- xreg[1:obsAll];
}
if(all(y[1:obsInSample]==xreg[1:obsInSample])){
warning(paste0("The exogenous variable and the forecasted data are exactly the same. ",
"What's the point of such a regression?"),
call.=FALSE);
xreg <- NULL;
}
# Number of exogenous variables
nExovars <- 1;
# Define matrix w for exogenous variables
matxt <- matrix(xreg,ncol=1);
# Define the second matat to fill in the coefs of the exogenous vars
matatMultiplicative <- matat <- matrix(NA,obsStates,1);
# Fill in the initial values for exogenous coefs using OLS
if(is.null(initialX)){
if(Etype=="M"){
matat[1:lagsModelMax,] <- cov(log(y[1:obsInSample][ot==1]),
xreg[1:obsInSample][ot==1])/var(xreg[1:obsInSample][ot==1]);
matatMultiplicative[1:lagsModelMax,] <- matat[1:lagsModelMax,];
}
else{
matat[1:lagsModelMax,] <- cov(y[1:obsInSample][ot==1],
xreg[1:obsInSample][ot==1])/var(xreg[1:obsInSample][ot==1]);
}
matat[] <- matat[1,]
# If Etype=="Z" or "C", estimate multiplicative stuff.
if(allowMultiplicative & all(Etype!=c("M","A"))){
matatMultiplicative[1:lagsModelMax,] <- cov(log(y[1:obsInSample][ot==1]),
xreg[1:obsInSample][ot==1])/var(xreg[1:obsInSample][ot==1]);
}
}
if(is.null(names(xreg))){
colnames(matxt) <- colnames(matat) <- colnames(matatMultiplicative) <- "x";
}
else{
xregNames <- gsub(" ", "_", names(xreg), fixed = TRUE);
colnames(matxt) <- colnames(matat) <- colnames(matatMultiplicative) <- xregNames
}
}
if(!is.null(xreg)){
xreg <- as.matrix(xreg);
}
}
##### The case with matrices and data frames
else if(is.matrix(xreg) | is.data.frame(xreg)){
if(is.data.frame(xreg)){
xreg <- as.matrix(xreg);
}
nExovars <- ncol(xreg);
if(nrow(xreg) < obsAll){
warning("xreg did not contain values for the holdout, so we had to predict missing values.",
call.=FALSE);
xregForecast <- matrix(NA,nrow=obsAll-nrow(xreg),ncol=nExovars);
if(!silent){
message("Producing forecasts for xreg variable...");
}
for(j in 1:nExovars){
if(!silent){
cat(paste0(rep("\b",nchar(round((j-1)/nExovars,2)*100)+1),collapse=""));
cat(paste0(round(j/nExovars,2)*100,"%"));
}
if(all((xreg[,j]==0) | (xreg[,j]==1))){
xregForecast[,j] <- oes(xreg[,j], model="MNN", h=obsAll-nrow(xreg), occurrence="o",ic="AIC")$forecast;
}
else{
xregForecast[,j] <- es(xreg[,j], h=obsAll-nrow(xreg), ic="AICc")$forecast;
}
}
xreg <- rbind(xreg,xregForecast);
if(!silent){
cat("\b\b\b\bDone!\n");
}
}
else if(nrow(xreg) > obsAll){
warning("xreg contained too many observations, so we had to cut off some of them.",
call.=FALSE);
xreg <- xreg[1:obsAll,];
}
xregEqualToData <- apply(xreg[1:obsInSample,]==y[1:obsInSample],2,all);
if(any(xregEqualToData)){
warning(paste0("One of exogenous variables and the forecasted data are exactly the same. ",
"We have dropped it."),
call.=FALSE);
xreg <- matrix(xreg[,!xregEqualToData],nrow=nrow(xreg),ncol=ncol(xreg)-1,
dimnames=list(NULL,colnames(xreg[,!xregEqualToData])));
}
nExovars <- ncol(xreg);
# If initialX is provided, then probably we don't need to check the xreg on variability and multicollinearity
if(is.null(initialX)){
checkvariability <- apply(matrix(xreg[1:obsInSample,][ot==1,]==rep(xreg[ot==1,][1,],
each=sum(ot)),sum(ot),
nExovars),2,all);
if(any(checkvariability)){
if(all(checkvariability)){
warning(paste0("None of exogenous variables has variability. ",
"Cannot do anything with that, so dropping out xreg."),
call.=FALSE);
xreg <- NULL;
nExovars <- 0;
}
else{
warning("Some exogenous variables do not have any variability. Dropping them out.",
call.=FALSE);
xreg <- as.matrix(xreg[,!checkvariability]);
nExovars <- ncol(xreg);
}
}
if(!is.null(xreg)){
# Check for multicollinearity and drop something if there is a perfect one
corMatrix <- cor(xreg);
corCheck <- upper.tri(corMatrix) & abs(corMatrix)>=0.999;
if(any(corCheck)){
removexreg <- unique(which(corCheck,arr.ind=TRUE)[,1]);
if(ncol(xreg)-length(removexreg)>1){
xreg <- xreg[,-removexreg];
}
else{
xreg <- matrix(xreg[,-removexreg],ncol=ncol(xreg)-length(removexreg),
dimnames=list(NULL,c(colnames(xreg)[-removexreg])));
}
nExovars <- ncol(xreg);
warning("Some exogenous variables were perfectly correlated. We've dropped them out.",
call.=FALSE);
}
# Check multiple correlations. This is needed for cases with dummy variables.
# In case with regressors="select" some of the perfectly correlated things,
# will be dropped out automatically.
if(nExovars>2 & (regressors=="u")){
corMatrix <- cor(xreg);
corMulti <- rep(NA,nExovars);
if(det(corMatrix)!=0){
for(i in 1:nExovars){
corMulti[i] <- 1 - det(corMatrix) / det(corMatrix[-i,-i]);
}
if(any(corMulti>=0.999)){
stop(paste0("Some combinations of exogenous variables are perfectly correlated. \n",
"If you use sets of dummy variables, don't forget to drop some of them."),
call.=FALSE);
}
}
else{
stop(paste0("Some combinations of exogenous variables are perfectly correlated. \n",
"If you use sets of dummy variables, don't forget to drop some of them."),
call.=FALSE);
}
}
}
}
if(!is.null(xreg)){
# mat.x is needed for the initial values of coefs estimation using OLS
mat.x <- as.matrix(cbind(rep(1,obsAll),xreg));
# Define the second matat to fill in the coefs of the exogenous vars
matatMultiplicative <- matat <- matrix(NA,obsStates,nExovars);
# Define matrix w for exogenous variables
matxt <- as.matrix(xreg);
# Fill in the initial values for exogenous coefs using OLS
if(is.null(initialX)){
if(Etype=="M"){
matat[1:lagsModelMax,] <- rep(t(solve(t(mat.x[1:obsInSample,][ot==1,]) %*%
mat.x[1:obsInSample,][ot==1,],tol=1e-50) %*%
t(mat.x[1:obsInSample,][ot==1,]) %*%
log(y[1:obsInSample][ot==1]))[2:(nExovars+1)],
each=lagsModelMax);
matatMultiplicative[1:lagsModelMax,] <- matat[1:lagsModelMax,];
}
else{
matat[1:lagsModelMax,] <- rep(t(solve(t(mat.x[1:obsInSample,][ot==1,]) %*%
mat.x[1:obsInSample,][ot==1,],tol=1e-50) %*%
t(mat.x[1:obsInSample,][ot==1,]) %*%
y[1:obsInSample][ot==1])[2:(nExovars+1)],
each=lagsModelMax);
}
matat[-1,] <- rep(matat[1,],each=obsStates-1);
# If Etype=="Z" or "C", estimate multiplicative stuff.
if(allowMultiplicative & all(Etype!=c("M","A"))){
matatMultiplicative[1:lagsModelMax,] <- rep(t(solve(t(mat.x[1:obsInSample,][ot==1,]) %*%
mat.x[1:obsInSample,][ot==1,],tol=1e-50) %*%
t(mat.x[1:obsInSample,][ot==1,]) %*%
log(y[1:obsInSample][ot==1]))[2:(nExovars+1)],
each=lagsModelMax);
}
}
if(is.null(colnames(xreg))){
colnames(matxt) <- colnames(matat) <- colnames(matatMultiplicative) <- paste0("x",c(1:nExovars));
}
else{
xregNames <- gsub(" ", "_", colnames(xreg), fixed = TRUE);
if(regressors=="s" & any(grepl('[^[:alnum:]]', xregNames))){
warning(paste0("There were some special characters in names of ",
"xreg variables. We had to remove them."),call.=FALSE);
xregNames <- gsub("[^[:alnum:]]", "", xregNames);
}
xregDuplicated <- duplicated(colnames(xreg));
if(any(xregDuplicated)){
warning(paste0("Some names of variables are duplicated. ",
"We had to rename them."),call.=FALSE);
xregNames[xregDuplicated] <- paste0("xDuplicated",c(1:sum(xregDuplicated)));
}
colnames(matxt) <- colnames(matat) <- colnames(matatMultiplicative) <- xregNames;
}
}
}
else{
stop("Unknown format of xreg. Should be either vector or matrix. Aborting!",call.=FALSE);
}
xregEstimate <- TRUE;
# Check the provided initialX vector
if(!is.null(initialX)){
if(!is.numeric(initialX) & !is.vector(initialX) & !is.matrix(initialX)){
stop("The initials for exogenous are not a numeric vector or a matrix!", call.=FALSE);
}
else{
if(length(initialX) != nExovars){
stop(paste0("The size of initial vector for exogenous is wrong!\n",
"It should correspond to the number of exogenous variables."), call.=FALSE);
}
else{
matat[1:lagsModelMax,] <- as.vector(rep(initialX,each=lagsModelMax));
initialXEstimate <- FALSE;
}
}
}
else{
initialXEstimate <- TRUE;
}
}
else{
updateX <- FALSE;
}
##### In case we changed xreg to null or if it was like that...
if(is.null(xreg)){
# "1" is needed for the final forecast simplification
nExovars <- 1;
matxt <- matrix(1,obsStates,1);
matatMultiplicative <- matat <- matrix(0,obsStates,1);
matFX <- matrix(1,1,1);
vecgX <- matrix(0,1,1);
xregEstimate <- FALSE;
FXEstimate <- FALSE;
gXEstimate <- FALSE;
initialXEstimate <- FALSE;
}
# Now check transition and persistence of exogenous variables
if(xregEstimate & updateX){
# First - transition matrix
if(!is.null(transitionX)){
if(!is.numeric(transitionX) & !is.vector(transitionX) & !is.matrix(transitionX)){
stop("Transition matrix for exogenous is not a numeric vector or a matrix!", call.=FALSE);
}
else{
if(length(transitionX) != nExovars^2){
stop(paste0("Size of transition matrix for exogenous is wrong!\n",
"It should correspond to the number of exogenous variables."), call.=FALSE);
}
else{
matFX <- matrix(transitionX,nExovars,nExovars);
FXEstimate <- FALSE;
}
}
}
else{
matFX <- diag(nExovars);
FXEstimate <- TRUE;
}
# Now - persistence vector
if(!is.null(persistenceX)){
if(!is.numeric(persistenceX) & !is.vector(persistenceX) & !is.matrix(persistenceX)){
stop("Persistence vector for exogenous is not numeric!", call.=FALSE);
}
else{
if(length(persistenceX) != nExovars){
stop(paste0("Size of persistence vector for exogenous is wrong!\n",
"It should correspond to the number of exogenous variables."), call.=FALSE);
}
else{
vecgX <- matrix(persistenceX,nExovars,1);
gXEstimate <- FALSE;
}
}
}
else{
vecgX <- matrix(0,nExovars,1);
gXEstimate <- TRUE;
}
}
else if(xregEstimate & !updateX){
matFX <- diag(nExovars);
FXEstimate <- FALSE;
vecgX <- matrix(0,nExovars,1);
gXEstimate <- FALSE;
}
if(all(!FXEstimate,!gXEstimate,!initialXEstimate)){
xregEstimate <- FALSE;
}
return(list(nExovars=nExovars, matxt=matxt, matat=matat, matFX=matFX, vecgX=vecgX,
xreg=xreg, xregEstimate=xregEstimate, FXEstimate=FXEstimate,
gXEstimate=gXEstimate, initialXEstimate=initialXEstimate,
matatMultiplicative=matatMultiplicative))
}
##### *Likelihood function* #####
likelihoodFunction <- function(B,yFittedSumLog=0){
#### Concentrated logLikelihood based on B and CF ####
logLikFromCF <- function(B, loss){
if(loss=="likelihood"){
return(-CFValue);
}
if(Etype=="M" && any(loss==c("TMSE","GTMSE","TMAE","GTMAE","THAM","GTHAM",
"GPL","aTMSE","aGTMSE","aGPL"))){
yFittedSumLog <- yFittedSumLog * h;
}
if(all(loss!=c("GTMSE","GTMAE","GTHAM","GPL","aGPL","aGTMSE"))){
CFValue[] <- log(CFValue);
}
if(any(loss==c("MAE","MAEh","MACE","TMAE","GTMAE"))){
return(- (obsInSample*(log(2) + 1 + CFValue) + obsZero) - yFittedSumLog);
}
else if(any(loss==c("HAM","HAMh","CHAM","THAM","GTHAM"))){
#### This is a temporary fix for the oes models... Needs to be done properly!!! ####
return(- 2*(obsInSample*(log(2) + 1 + CFValue) + obsZero) - yFittedSumLog);
}
else if(any(loss==c("GPL","aGPL","aGTMSE"))){
return(- 0.5 *(obsInSample*(h*log(2*pi) + 1 + CFValue) + obsZero) - yFittedSumLog);
}
else{
#if(loss==c("MSE","MSEh","MSCE")) obsNonzero
return(- 0.5 *(obsInSample*(log(2*pi) + 1 + CFValue) + obsZero) - yFittedSumLog);
}
}
CFValue <- CF(B);
if(any(occurrence==c("n","p"))){
return(logLikFromCF(B, loss));
}
else{
# Failsafe for exceptional cases when the probability is equal to zero / one,
# when it should not have been.
if(any(c(1-pFitted[ot==0]==0,pFitted[ot==1]==0))){
# return(-Inf);
ptNew <- pFitted[(pFitted!=0) & (pFitted!=1)];
otNew <- ot[(pFitted!=0) & (pFitted!=1)];
if(length(ptNew)==0){
return(logLikFromCF(B, loss));
}
else{
return(sum(log(ptNew[otNew==1])) + sum(log(1-ptNew[otNew==0]))
+ logLikFromCF(B, loss));
}
}
#Failsafe for cases, when data has no variability when ot==1.
if(CFValue==0){
if(loss=="GPL" | loss=="aGPL"){
return(sum(log(pFitted[ot==1]))*h + sum(log(1-pFitted[ot==0]))*h);
}
else{
return(sum(log(pFitted[ot==1])) + sum(log(1-pFitted[ot==0])));
}
}
if(rounded){
return(sum(log(pFitted[ot==1])) + sum(log(1-pFitted[ot==0])) - CFValue -
obsZero/2*(log(2*pi*B[length(B)]^2)+1));
}
if(loss=="GPL" | loss=="aGPL"){
return(sum(log(pFitted[ot==1]))*h
+ sum(log(1-pFitted[ot==0]))*h
+ logLikFromCF(B, loss));
}
else{
return(sum(log(pFitted[ot==1])) + sum(log(1-pFitted[ot==0]))
+ logLikFromCF(B, loss));
}
}
}
##### *Function calculates ICs* #####
ICFunction <- function(nParam=nParam,nParamOccurrence=nParamOccurrence,
B,Etype=Etype,yFittedSumLog=0){
# Information criteria are calculated with the constant part "log(2*pi*exp(1)*h+log(obs))*obs".
# And it is based on the mean of the sum squared residuals either than sum.
# Hyndman likelihood is: llikelihood <- obs*log(obs*cfObjective)
nParamOverall <- nParam + nParamOccurrence;
llikelihood <- likelihoodFunction(B,yFittedSumLog=yFittedSumLog);
# max here is needed in order to take into account cases with higher
## number of parameters than observations
### AICc and BICc are incorrect in case of non-normal residuals!
if(loss=="GPL"){
coefAIC <- 2*nParamOverall*h - 2*llikelihood;
coefBIC <- log(obsInSample)*nParamOverall*h - 2*llikelihood;
coefAICc <- (2*obsInSample*(nParam*h + (h*(h+1))/2) /
max(obsInSample - nParam - 1 - h,0)
-2*llikelihood);
coefBICc <- (((nParam + (h*(h+1))/2)*
log(obsInSample*h)*obsInSample*h) /
max(obsInSample*h - nParam - (h*(h+1))/2,0)
-2*llikelihood);
}
else{
coefAIC <- 2*nParamOverall - 2*llikelihood;
coefBIC <- log(obsInSample)*nParamOverall - 2*llikelihood;
coefAICc <- coefAIC + 2*nParam*(nParam+1) / max(obsInSample-nParam-1,0);
coefBICc <- (nParam * log(obsInSample) * obsInSample) / (obsInSample - nParam - 1) -2*llikelihood;
}
ICs <- c(coefAIC, coefAICc, coefBIC, coefBICc);
names(ICs) <- c("AIC", "AICc", "BIC", "BICc");
return(list(llikelihood=llikelihood,ICs=ICs));
}
##### *Ouptut printer* #####
ssOutput <- function(timeelapsed, modelname, persistence=NULL, transition=NULL, measurement=NULL,
phi=NULL, ARterms=NULL, MAterms=NULL, constant=NULL, a=NULL, b=NULL, initialType="o",
nParam=NULL, s2=NULL, hadxreg=FALSE, wentwild=FALSE,
loss="MSE", cfObjective=NULL, interval=FALSE, cumulative=FALSE,
intervalType=c("n","p","l","sp","np","a"), level=0.95, ICs,
holdout=FALSE, insideinterval=NULL, errormeasures=NULL,
occurrence="n", obs=NULL, digits=5){
# Function forms the generic output for state space models.
if(!is.null(modelname)){
if(is.list(modelname)){
model <- "smoothC";
modelname <- "Combined smooth";
}
else{
if(gregexpr("ETS",modelname)!=-1){
model <- "ETS";
}
else if(gregexpr("CES",modelname)!=-1){
model <- "CES";
}
else if(gregexpr("GUM",modelname)!=-1){
model <- "GUM";
}
else if(gregexpr("ARIMA",modelname)!=-1){
model <- "ARIMA";
}
else if(gregexpr("SMA",modelname)!=-1){
model <- "SMA";
}
else if(gregexpr("CMA",modelname)!=-1){
model <- "CMA";
}
}
}
else{
model <- "smoothC";
modelname <- "Combined smooth";
}
cat(paste0("Time elapsed: ",round(as.numeric(timeelapsed,units="secs"),2)," seconds\n"));
cat(paste0("Model estimated: ",modelname,"\n"));
if(all(occurrence!=c("n","none","p","provided"))){
if(any(occurrence==c("f","fixed"))){
occurrence <- "Fixed probability";
}
else if(any(occurrence==c("o","odds-ratio"))){
occurrence <- "Odds ratio";
}
else if(any(occurrence==c("i","inverse-odds-ratio"))){
occurrence <- "Inverse odds ratio";
}
else if(any(occurrence==c("d","direct"))){
occurrence <- "Direct";
}
else if(any(occurrence==c("g","general"))){
occurrence <- "General";
}
cat(paste0("Occurrence model type: ",occurrence));
cat("\n");
}
else if(any(occurrence==c("p"))){
cat(paste0("Occurrence data provided for the holdout.\n"));
}
### Stuff for ETS
if(any(model==c("ETS","GUM"))){
if(!is.null(persistence)){
cat(paste0("Persistence vector g:\n"));
if(is.matrix(persistence)){
print(round(t(persistence),digits));
}
else{
print(round(persistence,digits));
}
}
if(!is.null(phi)){
if(gregexpr("d",modelname)!=-1){
cat(paste0("Damping parameter: ", round(phi,digits),"\n"));
}
}
}
### Stuff for GUM
if(model=="GUM"){
if(!is.null(transition)){
cat("Transition matrix F: \n");
print(round(transition,digits));
}
if(!is.null(measurement)){
cat(paste0("Measurement vector w: ",paste(round(measurement,digits),collapse=", "),"\n"));
}
}
### Stuff for ARIMA
if(model=="ARIMA"){
if(all(!is.null(ARterms))){
cat("Matrix of AR terms:\n");
print(round(ARterms,digits));
}
if(all(!is.null(MAterms))){
cat("Matrix of MA terms:\n");
print(round(MAterms,digits));
}
if(!is.null(constant)){
if(constant!=FALSE){
cat(paste0("Constant value is: ",round(constant,digits),"\n"));
}
}
}
### Stuff for CES
if(model=="CES"){
if(!is.null(a)){
cat(paste0("a0 + ia1: ",round(a,digits),"\n"));
}
if(!is.null(b)){
if(is.complex(b)){
cat(paste0("b0 + ib1: ",round(b,digits),"\n"));
}
else{
cat(paste0("b: ",round(b,digits),"\n"));
}
}
}
if(model!="CMA"){
if(initialType=="o"){
cat("Initial values were optimised.\n");
}
else if(initialType=="b"){
cat("Initial values were produced using backcasting.\n");
}
else if(initialType=="p"){
cat("Initial values were provided by user.\n");
}
}
if(hadxreg){
cat("Xreg coefficients were estimated");
if(wentwild){
cat(" in a crazy style\n");
}
else{
cat(" in a normal style\n");
}
}
cat(paste0("\nLoss function type: ",loss))
if(!is.null(cfObjective)){
cat(paste0("; Loss function value: ",round(cfObjective,digits)));
}
if(!is.null(s2)){
cat("\nError standard deviation: "); cat(round(sqrt(s2),digits));
cat("\n");
}
cat("Sample size: "); cat(obs);
cat("\n");
if(!is.null(nParam)){
cat("Number of estimated parameters: "); cat(nParam[1,4]);
cat("\n");
if(nParam[2,4]>0){
cat("Number of provided parameters: "); cat(nParam[2,4]);
cat("\n");
}
cat("Number of degrees of freedom: "); cat(obs-nParam[1,4]);
cat("\n");
}
cat("Information criteria:\n");
if(model=="ETS"){
if(any(unlist(gregexpr("C",modelname))!=-1)){
cat("(combined values)\n");
}
ICs <- ICs[nrow(ICs),];
}
print(round(ICs,digits));
if(interval){
cat("\n");
if(intervalType=="p"){
intervalType <- "parametric";
}
else if(intervalType=="l"){
intervalType <- "likelihood-based";
}
else if(intervalType=="sp"){
intervalType <- "semiparametric";
}
else if(intervalType=="np"){
intervalType <- "nonparametric";
}
else if(intervalType=="a"){
intervalType <- "asymmetric";
}
if(cumulative){
intervalType <- paste0("cumulative ",intervalType);
}
cat(paste0(level*100,"% ",intervalType," prediction interval was constructed"));
}
if(holdout){
cat("\n");
if(interval && !is.null(insideinterval)){
cat(paste0(round(insideinterval,0), "% of values are in the prediction interval\n"));
}
cat("Forecast errors:\n");
if(any(occurrence==c("none","n"))){
cat(paste(paste0("MPE: ",round(errormeasures["MPE"],3)*100,"%"),
paste0("sCE: ",round(errormeasures["sCE"],3)*100,"%"),
paste0("Asymmetry: ",round(errormeasures["asymmetry"],3)*100,"%"),
paste0("MAPE: ",round(errormeasures["MAPE"],3)*100,"%\n"),sep="; "));
cat(paste(paste0("MASE: ",round(errormeasures["MASE"],3)),
paste0("sMAE: ",round(errormeasures["sMAE"],3)*100,"%"),
paste0("sMSE: ",round(errormeasures["sMSE"],3)*100,"%"),
paste0("rMAE: ",round(errormeasures["rMAE"],3)),
paste0("rRMSE: ",round(errormeasures["rRMSE"],3),"\n"),sep="; "));
}
else{
cat(paste(paste0("Asymmetry: ",round(errormeasures["asymmetry"],3)*100,"%"),
paste0("sMSE: ",round(errormeasures["sMSE"],3)*100,"%"),
paste0("rRMSE: ",round(errormeasures["rRMSE"],3)),
paste0("sPIS: ",round(errormeasures["sPIS"],3)*100,"%"),
paste0("sCE: ",round(errormeasures["sCE"],3)*100,"%\n"),sep="; "));
}
}
}
debugger <- function(){
continue <- readline(prompt="Continue? ")
if(continue=="n"){
stop("The user asked to stop the calculations.");
}
}
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Defines functions debugger ssOutput ICFunction likelihoodFunction ssXreg ssForecaster ssIntervals ssFitter ssAutoInput ssInput
utils::globalVariables(c("h","holdout","orders","lags","transition","measurement","multisteps","ot",
"obsInSample","obsAll","obsStates","obsNonzero","obsZero","pFitted","loss",
"CF","Etype","Ttype","Stype","matxt","matFX","vecgX","xreg","matvt","nExovars",
"matat","errors","nParam","interval","intervalType","level","model","oesmodel",
"imodel","constant","AR","MA","y","yFitted","cumulative","rounded"));
##### *Checker of input of basic functions* #####
ssInput <- function(smoothType=c("es","gum","ces","ssarima","smoothC"),...){
# This is universal function needed in order to check the passed arguments to es(), gum(),
# ces() and ssarima()
smoothType <- smoothType[1];
ellipsis <- list(...);
ParentEnvironment <- ellipsis[['ParentEnvironment']];
##### silent #####
silent <- silent[1];
# Fix for cases with TRUE/FALSE.
if(!is.logical(silent)){
if(all(silent!=c("none","all","graph","legend","output","debugging","n","a","g","l","o","d"))){
warning(paste0("Sorry, I have no idea what 'silent=",silent,"' means. Switching to 'none'."),
call.=FALSE);
silent <- "none";
}
silent <- substring(silent,1,1);
}
silentValue <- silent;
if(silentValue==FALSE | silentValue=="n"){
silentText <- FALSE;
silentGraph <- FALSE;
silentLegend <- FALSE;
}
else if(silentValue==TRUE | silentValue=="a"){
silentText <- TRUE;
silentGraph <- TRUE;
silentLegend <- TRUE;
}
else if(silentValue=="g"){
silentText <- FALSE;
silentGraph <- TRUE;
silentLegend <- TRUE;
}
else if(silentValue=="l"){
silentText <- FALSE;
silentGraph <- FALSE;
silentLegend <- TRUE;
}
else if(silentValue=="o"){
silentText <- TRUE;
silentGraph <- FALSE;
silentLegend <- FALSE;
}
else if(silentValue=="d"){
silentText <- TRUE;
silentGraph <- FALSE;
silentLegend <- FALSE;
}
# Check what was passed as a horizon
if(h<=0){
warning(paste0("You have set forecast horizon equal to ",h,". We hope you know, what you are doing."),
call.=FALSE);
if(h<0){
warning("And by the way, we can't do anything with negative horizon, so we will set it equal to zero.",
call.=FALSE);
h <- 0;
}
}
##### data #####
if(any(is.smooth.sim(y))){
y <- y$data;
}
else if(any(class(y)=="Mdata")){
h <- y$h;
holdout <- TRUE;
y <- ts(c(y$x,y$xx),start=start(y$x),frequency=frequency(y$x));
}
if(!is.numeric(y)){
stop("The provided data is not a vector or ts object! Can't construct any model!", call.=FALSE);
}
if(!is.null(ncol(y))){
if(ncol(y)>1){
stop("The provided data is not a vector! Can't construct any model!", call.=FALSE);
}
}
# Check the data for NAs
if(any(is.na(y))){
if(!silentText){
warning("Data contains NAs. These observations will be substituted by zeroes.",call.=FALSE);
}
y[is.na(y)] <- 0;
}
# Define obs, the number of observations of in-sample
obsInSample <- length(y) - holdout*h;
# Define obsAll, the overal number of observations (in-sample + holdout)
obsAll <- length(y) + (1 - holdout)*h;
# If obsInSample is negative, this means that we can't do anything...
if(obsInSample<=0){
stop("Not enough observations in sample.",call.=FALSE);
}
# Define the actual values
yInSample <- matrix(y[1:obsInSample],obsInSample,1);
dataFreq <- frequency(y);
dataStart <- start(y);
yForecastStart <- time(y)[obsInSample]+deltat(y);
# Number of parameters to estimate / provided
parametersNumber <- matrix(0,2,4,
dimnames=list(c("Estimated","Provided"),
c("nParamInternal","nParamXreg","nParamOccurrence","nParamAll")));
if(any(smoothType==c("es","oes"))){
##### model for ES #####
if(!is.character(model)){
stop(paste0("Something strange is provided instead of character object in model: ",
paste0(model,collapse=",")),call.=FALSE);
}
# Predefine models pool for a model selection
modelsPool <- NULL;
# Deal with the list of models. Check what has been provided. Stop if there is a mistake.
if(length(model)>1){
if(any(nchar(model)>4)){
stop(paste0("You have defined strange model(s) in the pool: ",
paste0(model[nchar(model)>4],collapse=",")),call.=FALSE);
}
else if(any(substr(model,1,1)!="A" & substr(model,1,1)!="M" & substr(model,1,1)!="C")){
stop(paste0("You have defined strange model(s) in the pool: ",
paste0(model[substr(model,1,1)!="A" & substr(model,1,1)!="M"],collapse=",")),
call.=FALSE);
}
else if(any(substr(model,2,2)!="N" & substr(model,2,2)!="A" &
substr(model,2,2)!="M" & substr(model,2,2)!="C")){
stop(paste0("You have defined strange model(s) in the pool: ",
paste0(model[substr(model,2,2)!="N" & substr(model,2,2)!="A" &
substr(model,2,2)!="M"],collapse=",")),call.=FALSE);
}
else if(any(substr(model,3,3)!="N" & substr(model,3,3)!="A" &
substr(model,3,3)!="M" & substr(model,3,3)!="d" & substr(model,3,3)!="C")){
stop(paste0("You have defined strange model(s) in the pool: ",
paste0(model[substr(model,3,3)!="N" & substr(model,3,3)!="A" &
substr(model,3,3)!="M" & substr(model,3,3)!="d"],collapse=",")),
call.=FALSE);
}
else if(any(nchar(model)==4 & substr(model,4,4)!="N" & substr(model,4,4)!="A" &
substr(model,4,4)!="M" & substr(model,4,4)!="C")){
stop(paste0("You have defined strange model(s) in the pool: ",
paste0(model[nchar(model)==4 & substr(model,4,4)!="N" &
substr(model,4,4)!="A" & substr(model,4,4)!="M"],collapse=",")),
call.=FALSE);
}
else{
modelsPoolCombiner <- (substr(model,1,1)=="C" | substr(model,2,2)=="C" |
substr(model,3,3)=="C" | substr(model,4,4)=="C");
modelsPool <- model[!modelsPoolCombiner];
modelsPool <- unique(modelsPool);
if(any(modelsPoolCombiner)){
if(any(substr(model,nchar(model),nchar(model))!="N")){
model <- "CCC";
}
else{
model <- "CCN";
}
}
else{
model <- c("Z","Z","Z");
if(all(substr(modelsPool,nchar(modelsPool),nchar(modelsPool))=="N")){
model[3] <- "N";
}
if(all(substr(modelsPool,2,2)=="N")){
model[2] <- "N";
}
model <- paste0(model,collapse="");
}
}
}
# If chosen model is "AAdN" or anything like that, we are taking the appropriate values
if(nchar(model)==4){
Etype <- substring(model,1,1);
Ttype <- substring(model,2,2);
Stype <- substring(model,4,4);
damped <- TRUE;
if(substring(model,3,3)!="d"){
message(paste0("You have defined a strange model: ",model));
sowhat(model);
model <- paste0(Etype,Ttype,"d",Stype);
}
}
else if(nchar(model)==3){
Etype <- substring(model,1,1);
Ttype <- substring(model,2,2);
Stype <- substring(model,3,3);
if(any(Ttype==c("Z","X","Y"))){
damped <- TRUE;
}
else{
damped <- FALSE;
}
}
else{
message(paste0("You have defined a strange model: ",model));
sowhat(model);
message("Switching to 'ZZZ'");
model <- "ZZZ";
Etype <- "Z";
Ttype <- "Z";
Stype <- "Z";
damped <- TRUE;
}
# Define if we want to select or combine models... or do none of the above.
if(is.null(modelsPool)){
if(any(unlist(strsplit(model,""))=="C")){
modelDo <- "combine";
if(Etype=="C"){
Etype <- "Z";
}
if(Ttype=="C"){
Ttype <- "Z";
}
if(Stype=="C"){
Stype <- "Z";
}
}
else if(any(unlist(strsplit(model,""))=="Z") |
any(unlist(strsplit(model,""))=="X") |
any(unlist(strsplit(model,""))=="Y")){
modelDo <- "select";
}
else{
modelDo <- "estimate";
}
if(Etype=="X"){
Etype <- "A";
}
else if(Etype=="Y"){
Etype <- "M";
}
}
else{
if(any(unlist(strsplit(model,""))=="C")){
modelDo <- "combine";
}
else{
modelDo <- "select";
}
}
### Check error type
if(all(Etype!=c("Z","X","Y","A","M","C"))){
warning(paste0("Wrong error type: ",Etype,". Should be 'Z', 'X', 'Y', 'A' or 'M'.\n",
"Changing to 'Z'"),call.=FALSE);
Etype <- "Z";
modelDo <- "select";
}
### Check trend type
if(all(Ttype!=c("Z","X","Y","N","A","M","C"))){
warning(paste0("Wrong trend type: ",Ttype,". Should be 'Z', 'X', 'Y', 'N', 'A' or 'M'.\n",
"Changing to 'Z'"),call.=FALSE);
Ttype <- "Z";
modelDo <- "select";
}
}
else if(any(smoothType==c("ssarima","msarima"))){
##### Orders and lags for ssarima #####
if(any(is.complex(c(ar.orders,i.orders,ma.orders,lags)))){
stop("Come on! Be serious! This is ARIMA, not CES!",call.=FALSE);
}
if(any(c(ar.orders,i.orders,ma.orders)<0)){
stop("Funny guy! How am I gonna construct a model with negative order?",call.=FALSE);
}
if(any(c(lags)<0)){
stop("Right! Why don't you try complex lags then, mister smart guy?",call.=FALSE);
}
# If there are zero lags, drop them
if(any(lags==0)){
ar.orders <- ar.orders[lags!=0];
i.orders <- i.orders[lags!=0];
ma.orders <- ma.orders[lags!=0];
lags <- lags[lags!=0];
}
if(any(lags>48) & (smoothType=="ssarima")){
warning(paste0("SSARIMA is quite slow with lags greater than 48. ",
"It is recommended to use MSARIMA in this case instead."),
call.=FALSE);
}
# Define maxorder and make all the values look similar (for the polynomials)
maxorder <- max(length(ar.orders),length(i.orders),length(ma.orders));
if(length(ar.orders)!=maxorder){
ar.orders <- c(ar.orders,rep(0,maxorder-length(ar.orders)));
}
if(length(i.orders)!=maxorder){
i.orders <- c(i.orders,rep(0,maxorder-length(i.orders)));
}
if(length(ma.orders)!=maxorder){
ma.orders <- c(ma.orders,rep(0,maxorder-length(ma.orders)));
}
if((length(lags)!=length(ar.orders)) & (length(lags)!=length(i.orders)) & (length(lags)!=length(ma.orders))){
stop("Seasonal lags do not correspond to any element of SARIMA",call.=FALSE);
}
# If zeroes are defined for some orders, drop them.
if(any((ar.orders + i.orders + ma.orders)==0)){
orders2leave <- (ar.orders + i.orders + ma.orders)!=0;
if(all(!orders2leave)){
orders2leave <- lags==min(lags);
}
ar.orders <- ar.orders[orders2leave];
i.orders <- i.orders[orders2leave];
ma.orders <- ma.orders[orders2leave];
lags <- lags[orders2leave];
}
# Get rid of duplicates in lags
if(length(unique(lags))!=length(lags)){
if(dataFreq!=1){
warning(paste0("'lags' variable contains duplicates: (",paste0(lags,collapse=","),
"). Getting rid of some of them."),call.=FALSE);
}
lagsNew <- unique(lags);
arOrdersNew <- iOrdersNew <- maOrdersNew <- lagsNew;
for(i in 1:length(lagsNew)){
arOrdersNew[i] <- max(ar.orders[which(lags==lagsNew[i])]);
iOrdersNew[i] <- max(i.orders[which(lags==lagsNew[i])]);
maOrdersNew[i] <- max(ma.orders[which(lags==lagsNew[i])]);
}
ar.orders <- arOrdersNew;
i.orders <- iOrdersNew;
ma.orders <- maOrdersNew;
lags <- lagsNew;
}
ARValue <- AR;
# Check the provided AR matrix / vector
if(!is.null(ARValue)){
if((!is.numeric(ARValue) | !is.vector(ARValue)) & !is.matrix(ARValue)){
warning(paste0("AR should be either vector or matrix. You have provided something strange...\n",
"AR will be estimated."),call.=FALSE);
ARRequired <- AREstimate <- TRUE;
ARValue <- NULL;
}
else{
if(is.matrix(ARValue)){
ARLength <- length(ARValue[ARValue!=0]);
}
else{
ARLength <- length(ARValue);
}
if(sum(ar.orders)!=ARLength){
warning(paste0("Wrong number of non-zero elements of AR. Should be ",sum(ar.orders),
" instead of ",length(ARValue[ARValue!=0]),".\n",
"AR will be estimated."),call.=FALSE);
ARRequired <- AREstimate <- TRUE;
ARValue <- NULL;
}
else{
if(is.matrix(ARValue)){
ARValue <- ARValue[ARValue!=0];
}
AREstimate <- FALSE;
ARRequired <- TRUE;
parametersNumber[2,1] <- parametersNumber[2,1] + length(ARValue);
}
}
}
else{
if(all(ar.orders==0)){
ARRequired <- AREstimate <- FALSE;
}
else{
ARRequired <- AREstimate <- TRUE;
}
}
MAValue <- MA;
# Check the provided MA matrix / vector
if(!is.null(MAValue)){
if((!is.numeric(MAValue) | !is.vector(MAValue)) & !is.matrix(MAValue)){
warning(paste0("MA should be either vector or matrix. You have provided something strange...\n",
"MA will be estimated."),call.=FALSE);
MARequired <- MAEstimate <- TRUE;
MAValue <- NULL;
}
else{
if(is.matrix(MAValue)){
MALength <- length(MAValue[MAValue!=0]);
}
else{
MALength <- length(MAValue);
}
if(sum(ma.orders)!=MALength){
warning(paste0("Wrong number of non-zero elements of MA. Should be ",sum(ma.orders),
" instead of ",length(MAValue[MAValue!=0]),".\n",
"MA will be estimated."),call.=FALSE);
MARequired <- MAEstimate <- TRUE;
MAValue <- NULL;
}
else{
if(is.matrix(MAValue)){
MAValue <- MAValue[MAValue!=0];
}
MAEstimate <- FALSE;
MARequired <- TRUE;
parametersNumber[2,1] <- parametersNumber[2,1] + length(MAValue);
}
}
}
else{
if(all(ma.orders==0)){
MARequired <- MAEstimate <- FALSE;
}
else{
MARequired <- MAEstimate <- TRUE;
}
}
constantValue <- constant;
# Check the provided constant
if(is.numeric(constantValue)){
constantEstimate <- FALSE;
constantRequired <- TRUE;
parametersNumber[2,1] <- parametersNumber[2,1] + 1;
}
else if(is.logical(constantValue)){
constantRequired <- constantEstimate <- constantValue;
constantValue <- NULL;
}
# Number of components to use
nComponents <- max(ar.orders %*% lags + i.orders %*% lags,ma.orders %*% lags);
# If there is no constant and there are no orders
if(nComponents==0 & !constantRequired){
constantValue <- 0;
constantRequired[] <- TRUE
nComponenst <- 1;
}
nonZeroARI <- matrix(1,ncol=2);
nonZeroMA <- matrix(1,ncol=2);
lagsModel <- matrix(rep(1,nComponents),ncol=1);
if(constantRequired){
lagsModel <- rbind(lagsModel,1);
}
lagsModelMax <- 1;
if(obsInSample < nComponents){
warning(paste0("In-sample size is ",obsInSample,", while number of components is ",nComponents,
". Cannot fit the model."),call.=FALSE)
stop("Not enough observations for such a complicated model.",call.=FALSE);
}
}
else if(smoothType=="ces"){
# If the user typed wrong seasonality, use the "Full" instead
if(all(seasonality!=c("n","s","p","f","none","simple","partial","full"))){
warning(paste0("Wrong seasonality type: '",seasonality, "'. Changing to 'full'"), call.=FALSE);
seasonality <- "f";
}
seasonality <- substring(seasonality[1],1,1);
}
if(any(smoothType==c("es","oes"))){
modelIsSeasonal <- Stype!="N";
# Check if the data is ts-object
if(!is.ts(y) & modelIsSeasonal){
if(!silentText){
message("The provided data is not ts object. Only non-seasonal models are available.");
}
Stype <- "N";
modelIsSeasonal <- FALSE;
substr(model,nchar(model),nchar(model)) <- "N";
}
### Check seasonality type
if(all(Stype!=c("Z","X","Y","N","A","M","C"))){
warning(paste0("Wrong seasonality type: ",Stype,". Should be 'Z', 'X', 'Y', 'N', 'A' or 'M'.",
"Setting to 'Z'."),call.=FALSE);
if(dataFreq==1){
Stype <- "N";
modelIsSeasonal <- FALSE;
}
else{
Stype <- "Z";
modelDo <- "select";
}
}
if(all(modelIsSeasonal,dataFreq==1)){
if(all(Stype!=c("Z","X","Y"))){
warning(paste0("Cannot build the seasonal model on data with frequency 1.\n",
"Switching to non-seasonal model: ETS(",substring(model,1,nchar(model)-1),"N)"));
}
Stype <- "N";
modelIsSeasonal <- FALSE;
}
# Check the pool of models to combine if it was decided that the data is not seasonal
if(!modelIsSeasonal && !is.null(modelsPool)){
modelsPool <- modelsPool[substr(modelsPool,nchar(modelsPool),nchar(modelsPool))=="N"];
}
}
else if(smoothType=="sma"){
lagsModelMax <- 1;
if(is.null(order)){
nParamMax <- obsInSample;
}
else{
nParamMax <- order;
}
}
else{
lagsModelMax <- 1;
nParamMax <- 0;
}
# Make sure that y is ts
if(!is.ts(y)){
y <- ts(y);
}
##### Lags and components for GUM #####
if(smoothType=="gum"){
if(any(is.complex(c(orders,lags)))){
stop("Complex values? Right! Come on! Be real!",call.=FALSE);
}
if(any(c(orders)<0)){
stop("Funny guy! How am I gonna construct a model with negative orders?",call.=FALSE);
}
if(any(c(lags)<0)){
stop("Right! Why don't you try complex lags then, mister smart guy?",call.=FALSE);
}
if(length(orders) != length(lags)){
stop(paste0("The length of 'lags' (",length(lags),
") differes from the length of 'orders' (",length(orders),")."), call.=FALSE);
}
# If there are zero lags, drop them
if(any(lags==0)){
orders <- orders[lags!=0];
lags <- lags[lags!=0];
}
# If zeroes are defined for some orders, drop them.
if(any(orders==0)){
lags <- lags[orders!=0];
orders <- orders[orders!=0];
}
# Get rid of duplicates in lags
if(length(unique(lags))!=length(lags)){
lagsNew <- unique(lags);
ordersNew <- lagsNew;
for(i in 1:length(lagsNew)){
ordersNew[i] <- max(orders[which(lags==lagsNew[i])]);
}
orders <- ordersNew;
lags <- lagsNew;
}
lagsModel <- matrix(rep(lags,times=orders),ncol=1);
lagsModelMax <- max(lagsModel);
nComponents <- sum(orders);
type <- substr(type[1],1,1);
if(type=="m"){
if(any(yInSample<=0)){
warning("Multiplicative model can only be used on positive data. Switching to the additive one.",
call.=FALSE);
modelIsMultiplicative <- FALSE;
type <- "a";
}
else{
yInSample <- log(yInSample);
modelIsMultiplicative <- TRUE;
}
}
else{
modelIsMultiplicative <- FALSE;
}
}
else if(any(smoothType==c("es","oes"))){
lagsModelMax <- dataFreq * modelIsSeasonal + 1 * (!modelIsSeasonal);
}
else if(smoothType=="ces"){
a <- list(value=a);
b <- list(value=b);
if(is.null(a$value)){
a$estimate <- TRUE;
}
else{
a$estimate <- FALSE;
if(!is.null(a$value)){
parametersNumber[2,1] <- parametersNumber[2,1] + length(Re(a$value)) + length(Im(a$value));
}
}
if(all(is.null(b$value),any(seasonality==c("p","f")))){
b$estimate <- TRUE;
}
else{
b$estimate <- FALSE;
if(!is.null(b$value)){
parametersNumber[2,1] <- parametersNumber[2,1] + length(Re(b$value)) + length(Im(b$value));
}
}
# Define lags, number of components and number of parameters
if(seasonality=="n"){
# No seasonality
lagsModelMax <- 1;
lagsModel <- c(1,1);
# Define the number of all the parameters (smoothing parameters + initial states). Used in AIC mainly!
nComponents <- 2;
a$number <- 2;
b$number <- 0;
}
else if(seasonality=="s"){
# Simple seasonality, lagged CES
lagsModelMax <- dataFreq;
lagsModel <- c(lagsModelMax,lagsModelMax);
nComponents <- 2;
a$number <- 2;
b$number <- 0;
}
else if(seasonality=="p"){
# Partial seasonality with a real part only
lagsModelMax <- dataFreq;
lagsModel <- c(1,1,lagsModelMax);
nComponents <- 3;
a$number <- 2;
b$number <- 1;
}
else if(seasonality=="f"){
# Full seasonality with both real and imaginary parts
lagsModelMax <- dataFreq;
lagsModel <- c(1,1,lagsModelMax,lagsModelMax);
nComponents <- 4;
a$number <- 2;
b$number <- 2;
}
}
#### This is a temporary thing. If the function works, we will do that properly ####
else if(smoothType=="msarima"){
# Define the non-zero values. This is done via the calculation of orders of polynomials
ariValues <- list(NA);
maValues <- list(NA);
for(i in 1:length(lags)){
ariValues[[i]] <- c(0,min(1,ar.orders[i]):ar.orders[i])
if(i.orders[i]!=0){
ariValues[[i]] <- c(ariValues[[i]],1:i.orders[i]+ar.orders[i]);
}
ariValues[[i]] <- unique(ariValues[[i]] * lags[i]);
maValues[[i]] <- unique(c(0,min(1,ma.orders[i]):ma.orders[i]) * lags[i]);
}
# Produce ARI polynomials
ariLengths <- unlist(lapply(ariValues,length));
ariPolynomial <- array(0,ariLengths);
for(i in 1:length(ariValues)){
if(i==1){
ariPolynomial <- ariPolynomial + array(ariValues[[i]], ariLengths);
}
else{
ariPolynomial <- ariPolynomial + array(rep(ariValues[[i]],each=prod(ariLengths[1:(i-1)])),
ariLengths);
}
}
# Produce MA polynomials
maLengths <- unlist(lapply(maValues,length));
maPolynomial <- array(0,maLengths);
for(i in 1:length(maValues)){
if(i==1){
maPolynomial <- maPolynomial + array(maValues[[i]], maLengths);
}
else{
maPolynomial <- maPolynomial + array(rep(maValues[[i]],each=prod(maLengths[1:(i-1)])),
maLengths);
}
}
# What are the non-zero ARI and MA polynomials?
### What are their positions in transition matrix?
nonZeroARI <- unique(matrix(c(ariPolynomial)[-1],ncol=1));
nonZeroMA <- unique(matrix(c(maPolynomial)[-1],ncol=1));
nonZeroComponents <- sort(unique(c(nonZeroARI,nonZeroMA)));
nonZeroARI <- cbind(nonZeroARI,which(nonZeroComponents %in% nonZeroARI)-1);
nonZeroMA <- cbind(nonZeroMA,which(nonZeroComponents %in% nonZeroMA)-1);
nComponents <- length(nonZeroComponents);
if(obsInSample < nComponents){
warning(paste0("In-sample size is ",obsInSample,", while number of components is ",nComponents,
". Cannot fit the model."),call.=FALSE)
stop("Not enough observations for such a complicated model.",call.=FALSE);
}
lagsModel <- matrix(nonZeroComponents,ncol=1);
if(constantRequired){
lagsModel <- rbind(lagsModel,1);
}
lagsModelMax <- max(lagsModel);
}
##### obsStates #####
# Define the number of rows that should be in the matvt
obsStates <- max(obsAll + lagsModelMax, obsInSample + 2*lagsModelMax);
##### bounds #####
bounds <- substring(bounds[1],1,1);
if(all(bounds!=c("n","a","r","u"))){
warning("Strange bounds are defined. Switching to 'admissible'.",call.=FALSE);
bounds <- "a";
}
if(bounds=="n"){
warning("You have defined bounds='none'. ",
"This is dangerous and might lead to an unstable model or even break the function. ",
"Hopefully, you know what you are doing :).",
call.=FALSE);
}
##### Information Criteria #####
ic <- ic[1];
if(all(ic!=c("AICc","AIC","BIC","BICc"))){
warning(paste0("Strange type of information criteria defined: ",ic,". Switching to 'AICc'."),
call.=FALSE);
ic <- "AICc";
}
##### Loss function type #####
loss <- loss[1];
if(any(loss==c("MSEh","TMSE","GTMSE","MSCE","MAEh","TMAE","GTMAE","MACE",
"HAMh","THAM","GTHAM","CHAM",
"GPL","aMSEh","aTMSE","aGTMSE","aGPL"))){
multisteps <- TRUE;
}
else if(any(loss==c("likelihood","MSE","MAE","HAM","Rounded"))){
multisteps <- FALSE;
}
else{
if(loss=="MSTFE"){
warning(paste0("This estimator has recently been renamed from \"MSTFE\" to \"TMSE\". ",
"Please, use the new name."),call.=FALSE);
multisteps <- TRUE;
loss <- "TMSE";
}
else if(loss=="GMSTFE"){
warning(paste0("This estimator has recently been renamed from \"GMSTFE\" to \"GTMSE\". ",
"Please, use the new name."),call.=FALSE);
multisteps <- TRUE;
loss <- "GTMSE";
}
else if(loss=="aMSTFE"){
warning(paste0("This estimator has recently been renamed from \"aMSTFE\" to \"aTMSE\". ",
"Please, use the new name."),call.=FALSE);
multisteps <- TRUE;
loss <- "aTMSE";
}
else if(loss=="aGMSTFE"){
warning(paste0("This estimator has recently been renamed from \"aGMSTFE\" to \"aGTMSE\". ",
"Please, use the new name."),call.=FALSE);
multisteps <- TRUE;
loss <- "aGTMSE";
}
else{
warning(paste0("Strange loss function specified: ",loss,". Switching to 'MSE'."),call.=FALSE);
loss <- "MSE";
multisteps <- FALSE;
}
}
lossOriginal <- loss;
##### interval, intervalType, level #####
#intervalType <- substring(intervalType[1],1,1);
intervalType <- interval[1];
# Check the provided type of interval
if(is.logical(intervalType)){
if(intervalType){
intervalType <- "p";
}
else{
intervalType <- "none";
}
}
if(all(intervalType!=c("p","l","s","n","a","sp","np","none","parametric","likelihood","semiparametric","nonparametric"))){
warning(paste0("Wrong type of interval: '",intervalType, "'. Switching to 'parametric'."),call.=FALSE);
intervalType <- "p";
}
if(any(intervalType==c("none","n"))){
intervalType <- "n";
interval <- FALSE;
}
else if(any(intervalType==c("parametric","p"))){
intervalType <- "p";
interval <- TRUE;
}
else if(any(intervalType==c("semiparametric","sp"))){
intervalType <- "sp";
interval <- TRUE;
}
else if(any(intervalType==c("nonparametric","np"))){
intervalType <- "np";
interval <- TRUE;
}
else if(any(intervalType==c("likelihood","l"))){
intervalType <- "l";
interval <- TRUE;
}
else{
interval <- TRUE;
}
if(level>1){
level <- level / 100;
}
##### Occurrence part of the model #####
if(is.oes(occurrence)){
occurrenceModel <- occurrence;
occurrence <- occurrenceModel$occurrence;
occurrenceModelProvided <- TRUE;
}
else if(is.list(occurrence)){
warning(paste0("occurrence is not of the class oes. ",
"We will try to extract the type of model, but cannot promise anything."),
call.=FALSE);
occurrenceModel <- modelType(occurrence);
occurrence <- occurrence$occurrence;
occurrenceModelProvided <- FALSE;
}
else if(is.null(occurrence)){
occurrence <- "none";
occurrenceModel <- "MNN";
occurrenceModelProvided <- FALSE;
}
else{
if(is.null(oesmodel) || is.na(oesmodel)){
occurrenceModel <- "MNN";
}
else{
occurrenceModel <- oesmodel;
}
occurrenceModelProvided <- FALSE;
}
if(smoothType!="oes"){
if(is.numeric(occurrence)){
# If it is data, then it should either correspond to the whole sample (in-sample + holdout)
# or be equal to forecating horizon.
if(any(occurrence!=1) && all(length(c(occurrence))!=c(h,obsAll))){
warning(paste0("Length of the provided future occurrences is ",length(c(occurrence)),
" while length of forecasting horizon is ",h,".\n",
"Where should we plug in the future occurences anyway?\n",
"Switching to occurrence='fixed'."),call.=FALSE);
occurrence <- "f";
ot <- (yInSample!=0)*1;
obsNonzero <- sum(ot);
obsZero <- obsInSample - obsNonzero;
yot <- matrix(yInSample[yInSample!=0],obsNonzero,1);
pFitted <- matrix(mean(ot),obsInSample,1);
pForecast <- matrix(1,h,1);
nParamOccurrence <- 1;
}
else if(all(occurrence==1)){
obsNonzero <- obsInSample;
obsZero <- 0;
pFitted <- ot <- rep(1,obsInSample);
yot <- yInSample;
pForecast <- matrix(1,h,1);
nParamOccurrence <- 0;
occurrence <- "n";
occurrenceModelProvided <- FALSE;
}
else{
if(any(occurrence<0,occurrence>1)){
warning(paste0("Parameter 'occurrence' should contain values between zero and one.\n",
"Converting to appropriate vector."),call.=FALSE);
occurrence <- (occurrence!=0)*1;
}
ot <- (yInSample!=0)*1;
obsNonzero <- sum(ot);
obsZero <- obsInSample - obsNonzero;
yot <- matrix(yInSample[yInSample!=0],obsNonzero,1);
if(length(occurrence)==obsAll){
pFitted <- occurrence[1:obsInSample];
pForecast <- occurrence[(obsInSample+1):(obsInSample+h)];
}
else{
pFitted <- matrix(ot,obsInSample,1);
pForecast <- matrix(occurrence,h,1);
}
# "p" stand for "provided", meaning that we have been provided the future data
occurrence <- "p";
nParamOccurrence <- 0;
}
}
else{
occurrence <- occurrence[1];
if(all(occurrence!=c("n","a","f","g","o","i","d",
"none","auto","fixed","general","odds-ratio","inverse-odds-ratio","direct"))){
warning(paste0("Strange type of occurrence model defined: '",occurrence,
"'. Switching to 'fixed'."),
call.=FALSE);
occurrence <- "f";
}
occurrence <- substring(occurrence[1],1,1);
environment(intermittentParametersSetter) <- environment();
intermittentParametersSetter(occurrence,ParentEnvironment=environment());
}
# If the data is not occurrence, let's assume that the parameter was switched unintentionally.
if(all(ot==1) & all(occurrence!=c("n","p","provided"))){
occurrence <- "n";
occurrenceModelProvided <- FALSE;
}
if(occurrenceModelProvided){
parametersNumber[2,3] <- nparam(occurrenceModel);
}
}
else{
obsNonzero <- obsInSample;
obsZero <- 0;
}
if(any(smoothType==c("es"))){
# Check if multiplicative models can be fitted
allowMultiplicative <- !((any(yInSample<=0) && occurrence=="n") |
(occurrence!="n" && any(yInSample<0)));
# If non-positive values are present, check if data is intermittent,
# if negatives are here, switch to additive models
if(!allowMultiplicative){
if(any(Etype==c("M","Y"))){
warning(paste0("Can't apply multiplicative model to non-positive data. ",
"Switching error type to 'A'"), call.=FALSE);
Etype <- ifelse(Etype=="M","A","X");
}
if(any(Ttype==c("M","Y"))){
warning(paste0("Can't apply multiplicative model to non-positive data. ",
"Switching trend type to 'A'"), call.=FALSE);
Ttype <- ifelse(Ttype=="M","A","X");
}
if(any(Stype==c("M","Y"))){
warning(paste0("Can't apply multiplicative model to non-positive data. ",
"Switching seasonality type to 'A'"), call.=FALSE);
Stype <- ifelse(Stype=="M","A","X");
}
if(!is.null(modelsPool)){
if(any(c(substr(modelsPool,1,1),
substr(modelsPool,2,2),
substr(modelsPool,nchar(modelsPool),nchar(modelsPool)))=="M")){
warning(paste0("Can't apply multiplicative model to non-positive data. ",
"Switching to additive."), call.=FALSE);
substr(modelsPool,1,1)[substr(modelsPool,1,1)=="M"] <- "A";
substr(modelsPool,2,2)[substr(modelsPool,2,2)=="M"] <- "A";
substr(modelsPool,nchar(modelsPool),
nchar(modelsPool))[substr(modelsPool,nchar(modelsPool),
nchar(modelsPool))=="M"] <- "A";
}
}
}
# Check the pool of models. Make sure that there are no duplicates
if(!is.null(modelsPool)){
modelsPool <- unique(modelsPool);
if(is.null(modelsPool)){
stop(paste0("Cannot combine the models, your pool is empty. ",
"Please, check the vector of models you provided."),
call.=FALSE);
}
}
}
if(any(smoothType==c("es","gum","oes"))){
##### persistence for ES & GUM #####
if(!is.null(persistence)){
if((!is.numeric(persistence) | !is.vector(persistence)) & !is.matrix(persistence)){
warning(paste0("Persistence is not a numeric vector!\n",
"Changing to estimation of persistence vector values."),call.=FALSE);
persistence <- NULL;
persistenceEstimate <- TRUE;
}
else{
if(any(smoothType==c("es","oes"))){
if(modelDo!="estimate"){
warning(paste0("Predefined persistence vector can only be used with ",
"preselected ETS model.\n",
"Changing to estimation of persistence vector values."),call.=FALSE);
persistence <- NULL;
persistenceEstimate <- TRUE;
}
else{
if(length(persistence)>3){
warning(paste0("Length of persistence vector is wrong! ",
"It should not be greater than 3.\n",
"Changing to estimation of persistence vector values."),
call.=FALSE);
persistence <- NULL;
persistenceEstimate <- TRUE;
}
else{
if(length(persistence)!=(1 + (Ttype!="N") + (modelIsSeasonal))){
warning(paste0("Wrong length of persistence vector. ",
"Should be ",(1 + (Ttype!="N") + (modelIsSeasonal)),
" instead of ",length(persistence),".\n",
"Changing to estimation of persistence vector values."),
call.=FALSE);
persistence <- NULL;
persistenceEstimate <- TRUE;
}
else{
persistence <- as.vector(persistence);
persistenceEstimate <- FALSE;
parametersNumber[2,1] <- parametersNumber[2,1] + length(persistence);
# bounds <- "n";
}
}
}
}
else if(smoothType=="gum"){
if(length(persistence) != nComponents){
warning(paste0("Wrong length of persistence vector. Should be ",nComponents,
" instead of ",length(persistence),".\n",
"Changing to estimation of persistence vector values."),call.=FALSE);
persistence <- NULL;
persistenceEstimate <- TRUE;
}
else{
persistenceEstimate <- FALSE;
parametersNumber[2,1] <- parametersNumber[2,1] + length(persistence);
}
}
}
}
else{
persistenceEstimate <- TRUE;
}
}
##### initials ####
# initial type can be: "o" - optimal, "b" - backcasting, "p" - provided.
initialValue <- initial;
if(is.character(initialValue)){
initialValue <- substring(initialValue[1],1,1);
if(all(initialValue!=c("o","b","p"))){
warning("You asked for a strange initial value. We don't do that here. Switching to optimal.",
call.=FALSE);
initialType <- "o";
}
else{
# if(smoothType=="msarima" & initialValue=="o"){
# initialValue <- "b";
# warning(paste0("We don't support optimisation of the initial states of MSARIMA. ",
# "Switching to 'backcasting'."),
# call.=FALSE);
# }
initialType <- initialValue;
}
initialValue <- NULL;
}
else if(is.null(initialValue)){
if(silentText){
message("Initial value is not selected. Switching to optimal.");
}
initialType <- "o";
}
else if(!is.null(initialValue)){
if(!is.numeric(initialValue)){
warning(paste0("Initial vector is not numeric!\n",
"Values of initial vector will be estimated."),call.=FALSE);
initialValue <- NULL;
initialType <- "o";
}
else{
if(any(smoothType==c("es","oes"))){
if(modelDo!="estimate"){
warning(paste0("Predefined initials vector can only be used with preselected ETS model.\n",
"Changing to estimation of initials."),call.=FALSE);
initialValue <- NULL;
initialType <- "o";
}
else{
if(length(initialValue)>2){
warning(paste0("Length of initial vector is wrong! It should not be greater than 2.\n",
"Values of initial vector will be estimated."),call.=FALSE);
initialValue <- NULL;
initialType <- "o";
}
else{
if(length(initialValue) != (1*(Ttype!="N") + 1)){
warning(paste0("Length of initial vector is wrong! It should be ",
(1*(Ttype!="N") + 1),
" instead of ",length(initialValue),".\n",
"Values of initial vector will be estimated."),call.=FALSE);
initialValue <- NULL;
initialType <- "o";
}
else{
initialType <- "p";
# initialValue <- initial;
parametersNumber[2,1] <- parametersNumber[2,1] + length(initial);
}
}
}
}
else if(smoothType=="gum"){
if(length(initialValue) != (nComponents*max(lags))){
warning(paste0("Wrong length of initial vector. Should be ",(nComponents*max(lags)),
" instead of ",length(initial),".\n",
"Values of initial vector will be estimated."),call.=FALSE);
initialValue <- NULL;
initialType <- "o";
}
else{
initialType <- "p";
initialValue <- initial;
parametersNumber[2,1] <- parametersNumber[2,1] + orders %*% lags;
}
}
else if(smoothType=="ssarima"){
if(length(initialValue) != nComponents){
warning(paste0("Wrong length of initial vector. Should be ",nComponents,
" instead of ",length(initial),".\n",
"Values of initial vector will be estimated."),call.=FALSE);
initialValue <- NULL;
initialType <- "o";
}
else{
initialType <- "p";
initialValue <- initial;
parametersNumber[2,1] <- parametersNumber[2,1] + length(initial);
}
}
else if(smoothType=="msarima"){
if(length(initialValue) != nComponents*lagsModelMax){
warning(paste0("Wrong length of initial vector. Should be ",nComponents,
" instead of ",length(initial),".\n",
"Values of initial vector will be backcasted."),call.=FALSE);
initialValue <- NULL;
initialType <- "b";
}
else{
initialType <- "p";
# initialValue <- initial;
parametersNumber[2,1] <- parametersNumber[2,1] + length(initial);
}
}
else if(smoothType=="ces"){
if(length(initialValue) != lagsModelMax*nComponents){
warning(paste0("Wrong length of initial vector. Should be ",lagsModelMax*nComponents,
" instead of ",length(initial),".\n",
"Values of initial vector will be estimated."),call.=FALSE);
initialValue <- NULL;
initialType <- "o";
}
else{
initialType <- "p";
# initialValue <- initial;
parametersNumber[2,1] <- (parametersNumber[2,1] + 2*(seasonality!="s") +
lagsModelMax*(seasonality!="n") +
lagsModelMax*any(seasonality==c("f","s")));
}
}
else if(smoothType=="smoothC"){
warning("We cannot use the preset initials for the models. Switching to optimal.",
call.=FALSE);
initialType <- "o";
initialValue <- NULL;
}
}
}
if(any(smoothType==c("es","oes"))){
# If model selection is chosen, forget about the initial values and persistence
if(any(Etype=="Z",any(Ttype==c("X","Y","Z")),Stype=="Z")){
if(any(!is.null(initialValue),!is.null(initialSeason),!is.null(persistence),!is.null(phi))){
warning(paste0("Model selection doesn't go well with the predefined values.\n",
"Switching to estimation of all the parameters."),call.=FALSE);
initialValue <- NULL;
initialType <- "o";
initialSeason <- NULL;
persistence <- NULL;
phi <- NULL;
}
}
##### initialSeason for ES #####
if(!is.null(initialSeason)){
if(!is.numeric(initialSeason)){
warning(paste0("InitialSeason vector is not numeric!\n",
"Values of initialSeason vector will be estimated."),call.=FALSE);
initialSeason <- NULL;
initialSeasonEstimate <- TRUE;
}
else{
if(length(initialSeason)!=dataFreq){
warning(paste0("The length of initialSeason vector is wrong! ",
"It should correspond to the frequency of the data.\n",
"Values of initialSeason vector will be estimated."),call.=FALSE);
initialSeason <- NULL;
initialSeasonEstimate <- TRUE
}
else{
initialSeasonEstimate <- FALSE;
parametersNumber[2,1] <- parametersNumber[2,1] + length(initialSeason);
}
}
}
else{
initialSeasonEstimate <- TRUE;
}
# Check the length of the provided data. Say bad words if:
# 1. Seasonal model, <2 seasons of data and no initial seasonals.
# 2. Seasonal model, <1 season of data, no initial seasonals and no persistence.
if(is.null(modelsPool)){
if((modelIsSeasonal & (obsInSample < 2*dataFreq) & is.null(initialSeason)) |
(modelIsSeasonal & (obsInSample < dataFreq) & is.null(initialSeason) & is.null(persistence))){
if(is.null(initialSeason)){
warning(paste0("Sorry, but we don't have enough observations for the seasonal model!\n",
"Switching to non-seasonal."),call.=FALSE);
Stype <- "N";
modelIsSeasonal <- FALSE;
initialSeasonEstimate <- FALSE;
}
}
}
##### phi for ES #####
if(!is.null(phi)){
if(!is.numeric(phi) & (damped)){
warning(paste0("Provided value of phi is meaningless. phi will be estimated."),
call.=FALSE);
phi <- NULL;
phiEstimate <- TRUE;
}
else if(is.numeric(phi) & (phi<0 | phi>2)){
warning(paste0("Damping parameter should lie in (0, 2) region. ",
"Changing to the estimation of phi."),call.=FALSE);
phi <- NULL;
phiEstimate <- TRUE;
}
else{
phiEstimate <- FALSE;
if(damped){
parametersNumber[2,1] <- parametersNumber[2,1] + 1;
}
}
}
else{
if(damped){
phiEstimate <- TRUE;
}
else{
phiEstimate <- FALSE;
}
}
}
if(smoothType=="gum"){
##### transition for GUM #####
# Check the provided vector of initials: length and provided values.
if(!is.null(transition)){
if((!is.numeric(transition) | !is.vector(transition)) & !is.matrix(transition)){
warning(paste0("Transition matrix is not numeric!\n",
"The matrix will be estimated!"),call.=FALSE);
transitionEstimate <- TRUE;
}
else if(length(transition) != nComponents^2){
warning(paste0("Wrong length of transition matrix. Should be ",nComponents^2,
" instead of ",length(transition),".\n",
"The matrix will be estimated!"),call.=FALSE);
transitionEstimate <- TRUE;
}
else{
transitionEstimate <- FALSE;
parametersNumber[2,1] <- parametersNumber[2,1] + length(transition);
}
}
else{
transitionEstimate <- TRUE;
}
##### measurement for GUM #####
if(!is.null(measurement)){
if((!is.numeric(measurement) | !is.vector(measurement)) & !is.matrix(measurement)){
warning(paste0("Measurement vector is not numeric!\n",
"The vector will be estimated!"),call.=FALSE);
measurementEstimate <- TRUE;
}
else if(length(measurement) != nComponents){
warning(paste0("Wrong length of measurement vector. Should be ",nComponents,
" instead of ",length(measurement),".\n",
"The vector will be estimated!"),call.=FALSE);
measurementEstimate <- TRUE;
}
else{
measurementEstimate <- FALSE;
parametersNumber[2,1] <- parametersNumber[2,1] + length(measurement);
}
}
else{
measurementEstimate <- TRUE;
}
}
if(smoothType=="ssarima"){
if((nComponents==0) & (!constantRequired)){
if(!silentText){
warning("You have not defined any model! Constructing model with zero constant.",
call.=FALSE);
}
constantRequired <- TRUE;
constantValue <- 0;
initialType <- "p";
}
}
##### Calculate nParamMax for checks #####
if(any(smoothType==c("es","oes"))){
# 1: estimation of variance;
# 1 - 3: persitence vector;
# 1 - 2: initials;
# 1 - 1 phi value;
# dataFreq: dataFreq initials for seasonal component;
nParamMax <- (1 + (1 + (Ttype!="N") + (modelIsSeasonal))*persistenceEstimate +
(1 + (Ttype!="N"))*(initialType=="o") + phiEstimate*damped +
dataFreq*(modelIsSeasonal)*initialSeasonEstimate*(initialType!="b"));
}
else if(smoothType=="gum"){
nParamMax <- (1 + nComponents*measurementEstimate + nComponents*persistenceEstimate +
(nComponents^2)*transitionEstimate + (orders %*% lags)*(initialType=="o"));
}
else if(smoothType=="ssarima"){
nParamMax <- (1 + nComponents*(initialType=="o") + sum(ar.orders)*ARRequired*AREstimate +
sum(ma.orders)*MARequired*MAEstimate + constantRequired*constantEstimate);
}
else if(smoothType=="ces"){
nParamMax <- (1 + sum(lagsModel)*(initialType=="o") + a$number*a$estimate + b$number*b$estimate);
}
# Stop if number of observations is less than horizon and multisteps is chosen.
if((multisteps) & (obsNonzero < h+1) & all(loss!=c("aMSEh","aTMSE","aGTMSE","aGPL"))){
warning(paste0("Do you seriously think that you can use ",loss,
" with h=",h," on ",obsNonzero," non-zero observations?!"),call.=FALSE);
stop("Not enough observations for multisteps loss function.",call.=FALSE);
}
else if((multisteps) & (obsNonzero < 2*h) & all(loss!=c("aMSEh","aTMSE","aGTMSE","aGPL"))){
warning(paste0("Number of observations is really low for a multisteps loss function! ",
"We will, try but cannot guarantee anything..."),call.=FALSE);
}
normalizer <- mean(abs(diff(c(yInSample))));
##### Define regressors #####
if(smoothType!="sma"){
if(!any(regressors==c("use","select","u","s"))){
warning("Wrong type of regressors parameter. Changing to 'select'.", call.=FALSE);
regressors <- "select";
}
regressors <- substr(regressors[1],1,1);
}
if(is.null(xreg)){
regressors <- "u";
}
##### Fisher Information #####
if(!exists("FI",envir=ParentEnvironment,inherits=FALSE)){
FI <- FALSE;
}
else{
if(!is.logical(FI)){
FI <- FALSE;
}
if(!requireNamespace("numDeriv",quietly=TRUE) & FI){
warning(paste0("Sorry, but you don't have 'numDeriv' package, ",
"which is required in order to produce Fisher Information."),
call.=FALSE);
FI <- FALSE;
}
}
##### Rounded up values #####
if(!exists("rounded",envir=ParentEnvironment,inherits=FALSE)){
rounded <- FALSE;
}
else{
if(!is.logical(rounded)){
rounded <- FALSE;
}
else{
if(rounded){
loss <- "Rounded";
lossOriginal <- loss;
}
}
}
##### Return values to previous environment #####
assign("h",h,ParentEnvironment);
assign("holdout",holdout,ParentEnvironment);
assign("silentText",silentText,ParentEnvironment);
assign("silentGraph",silentGraph,ParentEnvironment);
assign("silentLegend",silentLegend,ParentEnvironment);
assign("obsInSample",obsInSample,ParentEnvironment);
assign("obsAll",obsAll,ParentEnvironment);
assign("obsStates",obsStates,ParentEnvironment);
assign("obsNonzero",obsNonzero,ParentEnvironment);
assign("obsZero",obsZero,ParentEnvironment);
assign("y",y,ParentEnvironment);
assign("yInSample",yInSample,ParentEnvironment);
assign("dataFreq",dataFreq,ParentEnvironment);
assign("dataStart",dataStart,ParentEnvironment);
assign("yForecastStart",yForecastStart,ParentEnvironment);
assign("bounds",bounds,ParentEnvironment);
assign("loss",loss,ParentEnvironment);
assign("lossOriginal",lossOriginal,ParentEnvironment);
assign("multisteps",multisteps,ParentEnvironment);
assign("intervalType",intervalType,ParentEnvironment);
assign("interval",interval,ParentEnvironment);
assign("initialValue",initialValue,ParentEnvironment);
assign("initialType",initialType,ParentEnvironment);
assign("normalizer",normalizer,ParentEnvironment);
assign("nParamMax",nParamMax,ParentEnvironment);
assign("regressors",regressors,ParentEnvironment);
assign("FI",FI,ParentEnvironment);
assign("rounded",rounded,ParentEnvironment);
assign("parametersNumber",parametersNumber,ParentEnvironment);
assign("ic",ic,ParentEnvironment);
#### intermittent part of the model... outdated ####
if(smoothType!="oes"){
assign("occurrence",occurrence,ParentEnvironment);
assign("occurrenceModel",occurrenceModel,ParentEnvironment);
assign("ot",ot,ParentEnvironment);
assign("yot",yot,ParentEnvironment);
assign("pFitted",pFitted,ParentEnvironment);
assign("pForecast",pForecast,ParentEnvironment);
assign("nParamOccurrence",nParamOccurrence,ParentEnvironment);
assign("occurrenceModelProvided",occurrenceModelProvided,ParentEnvironment);
}
else{
assign("initialSeasonEstimate",initialSeasonEstimate,ParentEnvironment);
assign("modelIsSeasonal",modelIsSeasonal,ParentEnvironment);
}
if(any(smoothType==c("es","oes"))){
assign("model",model,ParentEnvironment);
assign("modelsPool",modelsPool,ParentEnvironment);
assign("Etype",Etype,ParentEnvironment);
assign("Ttype",Ttype,ParentEnvironment);
assign("Stype",Stype,ParentEnvironment);
assign("damped",damped,ParentEnvironment);
assign("modelDo",modelDo,ParentEnvironment);
assign("initialSeason",initialSeason,ParentEnvironment);
assign("phi",phi,ParentEnvironment);
assign("phiEstimate",phiEstimate,ParentEnvironment);
if(smoothType=="es"){
assign("allowMultiplicative",allowMultiplicative,ParentEnvironment);
}
}
else if(smoothType=="gum"){
assign("transitionEstimate",transitionEstimate,ParentEnvironment);
assign("measurementEstimate",measurementEstimate,ParentEnvironment);
assign("orders",orders,ParentEnvironment);
assign("lags",lags,ParentEnvironment);
assign("modelIsMultiplicative",modelIsMultiplicative,ParentEnvironment);
}
else if(any(smoothType==c("ssarima","msarima"))){
assign("ar.orders",ar.orders,ParentEnvironment);
assign("i.orders",i.orders,ParentEnvironment);
assign("ma.orders",ma.orders,ParentEnvironment);
assign("lags",lags,ParentEnvironment);
assign("ARValue",ARValue,ParentEnvironment);
assign("ARRequired",ARRequired,ParentEnvironment);
assign("AREstimate",AREstimate,ParentEnvironment);
assign("MAValue",MAValue,ParentEnvironment);
assign("MARequired",MARequired,ParentEnvironment);
assign("MAEstimate",MAEstimate,ParentEnvironment);
assign("constantValue",constantValue,ParentEnvironment);
assign("constantEstimate",constantEstimate,ParentEnvironment);
assign("constantRequired",constantRequired,ParentEnvironment);
assign("nonZeroARI",nonZeroARI,ParentEnvironment);
assign("nonZeroMA",nonZeroMA,ParentEnvironment);
}
else if(smoothType=="ces"){
assign("seasonality",seasonality,ParentEnvironment);
assign("a",a,ParentEnvironment);
assign("b",b,ParentEnvironment);
}
if(any(smoothType==c("es","oes","gum"))){
assign("persistence",persistence,ParentEnvironment);
assign("persistenceEstimate",persistenceEstimate,ParentEnvironment);
}
if(any(smoothType==c("gum","ssarima","ces","msarima"))){
assign("nComponents",nComponents,ParentEnvironment);
assign("lagsModelMax",lagsModelMax,ParentEnvironment);
assign("lagsModel",lagsModel,ParentEnvironment);
}
}
##### *Checker for auto. functions* #####
ssAutoInput <- function(smoothType=c("auto.ces","auto.gum","auto.ssarima","auto.msarima"),...){
# This is universal function needed in order to check the passed arguments to auto.ces(),
# auto.gum() and auto.ssarima()
ellipsis <- list(...);
ParentEnvironment <- ellipsis[['ParentEnvironment']];
##### silent #####
silent <- silent[1];
# Fix for cases with TRUE/FALSE.
if(!is.logical(silent)){
if(all(silent!=c("none","all","graph","legend","output","debugging","n","a","g","l","o","d"))){
message(paste0("Sorry, I have no idea what 'silent=",silent,"' means. Switching to 'none'."));
silent <- "none";
}
silent <- substring(silent,1,1);
}
silentValue <- silent;
if(silentValue==FALSE | silentValue=="n"){
silentText <- FALSE;
silentGraph <- FALSE;
silentLegend <- FALSE;
}
else if(silentValue==TRUE | silentValue=="a"){
silentText <- TRUE;
silentGraph <- TRUE;
silentLegend <- TRUE;
}
else if(silentValue=="g"){
silentText <- FALSE;
silentGraph <- TRUE;
silentLegend <- TRUE;
}
else if(silentValue=="l"){
silentText <- FALSE;
silentGraph <- FALSE;
silentLegend <- TRUE;
}
else if(silentValue=="o"){
silentText <- TRUE;
silentGraph <- FALSE;
silentLegend <- FALSE;
}
else if(silentValue=="d"){
silentText <- TRUE;
silentGraph <- FALSE;
silentLegend <- FALSE;
}
# Check what was asked as a horizon
if(h<=0){
warning(paste0("You have set forecast horizon equal to ",h,". We hope you know, what you are doing."),
call.=FALSE);
if(h<0){
warning(paste0("And by the way, we can't do anything with negative horizon, ",
"so we will set it equal to zero."),
call.=FALSE);
h <- 0;
}
}
##### Fisher Information #####
if(!exists("FI",envir=ParentEnvironment,inherits=FALSE)){
FI <- FALSE;
}
##### data #####
if(any(is.smooth.sim(y))){
y <- y$data;
}
else if(any(class(y)=="Mdata")){
h <- y$h;
holdout <- TRUE;
y <- ts(c(y$x,y$xx),start=start(y$x),frequency=frequency(y$x));
}
if(!is.numeric(y)){
stop("The provided data is not a vector or ts object! Can't build any model!", call.=FALSE);
}
# Check the data for NAs
if(any(is.na(y))){
if(!silentText){
warning("Data contains NAs. These observations will be substituted by zeroes.",
call.=FALSE);
}
y[is.na(y)] <- 0;
}
##### Observations #####
# Define obs, the number of observations of in-sample
obsInSample <- length(y) - holdout*h;
# Define obsAll, the overal number of observations (in-sample + holdout)
obsAll <- length(y) + (1 - holdout)*h;
yInSample <- matrix(y[1:obsInSample],obsInSample,1);
dataFreq <- frequency(y);
dataStart <- start(y);
yForecastStart <- time(y)[obsInSample]+deltat(y);
# This is the critical minimum needed in order to at least fit ARIMA(0,0,0) with constant
if(obsInSample < 4){
stop("Sorry, but your sample is too small. Come back when you have at least 4 observations...",
call.=FALSE);
}
# Check the provided vector of initials: length and provided values.
initialValue <- initial;
if(is.character(initialValue)){
initialValue <- substring(initialValue[1],1,1);
if(initialValue!="o" & initialValue!="b"){
warning("You asked for a strange initial value. We don't do that here. Switching to optimal.",
call.=FALSE);
initialType <- "o";
}
else{
initialType <- initialValue;
}
initialValue <- NULL;
}
else if(is.null(initialValue)){
if(!silentText){
warning("Initial value is not selected. Switching to optimal.",call.=FALSE);
}
initialType <- "o";
}
else{
warning("Predefined initials don't go well with automatic model selection. Switching to optimal.",
call.=FALSE);
initialType <- "o";
initialValue <- NULL;
}
##### bounds #####
bounds <- substring(bounds[1],1,1);
# Check if "bounds" parameter makes any sense
if(all(bounds!=c("n","a","r"))){
warning("Strange bounds are defined. Switching to 'admissible'.",call.=FALSE);
bounds <- "a";
}
if(bounds=="n"){
warning("You have defined bounds='none'. ",
"This is dangerous and might lead to an unstable model or even break the function. ",
"Hopefully, you know what you are doing :).",
call.=FALSE);
}
##### Information Criteria #####
ic <- ic[1];
if(all(ic!=c("AICc","AIC","BIC","BICc"))){
warning(paste0("Strange type of information criteria defined: ",ic,". Switching to 'AICc'."),
call.=FALSE);
ic <- "AICc";
}
##### Loss function type #####
loss <- loss[1];
if(any(loss==c("MSEh","TMSE","GTMSE","MSCE","MAEh","TMAE","GTMAE","MACE",
"HAMh","THAM","GTHAM","CHAM",
"GPL","aMSEh","aTMSE","aGTMSE","aGPL"))){
multisteps <- TRUE;
}
else if(any(loss==c("likelihood","MSE","MAE","HAM","Rounded"))){
multisteps <- FALSE;
}
else{
warning(paste0("Strange loss function specified: ",loss,". Switching to 'MSE'."),
call.=FALSE);
loss <- "MSE";
multisteps <- FALSE;
}
if(!any(loss==c("likelihood","MSE","MAE","HAM","MSEh","MAEh","HAMh","MSCE","MACE","CHAM",
"GPL","aGPL"))){
warning(paste0("'",loss,"' is used as loss function instead of 'likelihood'. ",
"The results of the model selection may be wrong."),
call.=FALSE);
}
##### interval, intervalType, level #####
intervalType <- interval[1];
# Check the provided type of interval
if(is.logical(intervalType)){
if(intervalType){
intervalType <- "p";
}
else{
intervalType <- "none";
}
}
if(all(intervalType!=c("p","l","s","n","a","sp","np","none","parametric","likelihood","semiparametric","nonparametric"))){
warning(paste0("Wrong type of interval: '",intervalType, "'. Switching to 'parametric'."),call.=FALSE);
intervalType <- "p";
}
if(any(intervalType==c("none","n"))){
intervalType <- "n";
interval <- FALSE;
}
else if(any(intervalType==c("parametric","p"))){
intervalType <- "p";
interval <- TRUE;
}
else if(any(intervalType==c("semiparametric","sp"))){
intervalType <- "sp";
interval <- TRUE;
}
else if(any(intervalType==c("nonparametric","np"))){
intervalType <- "np";
interval <- TRUE;
}
else if(any(intervalType==c("likelihood","l"))){
intervalType <- "l";
interval <- TRUE;
}
else{
interval <- TRUE;
}
##### Occurrence part of the model #####
if(is.oes(occurrence)){
occurrenceModel <- occurrence;
occurrence <- occurrenceModel$occurrence;
occurrenceModelProvided <- TRUE;
}
else if(is.list(occurrence)){
warning(paste0("occurrence is not of the class oes. ",
"We will try to extract the type of model, but cannot promise anything."),
call.=FALSE);
occurrenceModel <- modelType(occurrence);
occurrence <- occurrenceModel$occurrence;
occurrenceModelProvided <- FALSE;
}
else if(is.null(occurrence)){
occurrence <- "none";
occurrenceModel <- "MNN";
occurrenceModelProvided <- FALSE;
}
else{
if(is.null(oesmodel) || is.na(oesmodel)){
occurrenceModel <- "MNN";
}
else{
occurrenceModel <- oesmodel;
}
occurrenceModelProvided <- FALSE;
}
if(exists("intermittent",envir=ParentEnvironment,inherits=FALSE)){
intermittent <- substr(intermittent[1],1,1);
warning("The parameter \"intermittent\" is obsolete. Please, use \"occurrence\" instead");
occurrence <- switch(intermittent,
"l"="o",
"p"="d",
"f"="f",
"n"="n",
"a"="a",
"i"=,
"s"="i");
}
if(exists("imodel",envir=ParentEnvironment,inherits=FALSE)){
if(!is.null(imodel)){
oesmodel <- imodel;
warning("The parameter \"imodel\" is obsolete. Please, use \"oesmodel\" instead");
}
else{
oesmodel <- imodel;
}
}
if(is.numeric(occurrence)){
obsNonzero <- sum((yInSample!=0)*1);
# If it is data, then it should either correspond to the whole sample (in-sample + holdout)
# or be equal to forecating horizon.
if(all(length(c(occurrence))!=c(h,obsAll))){
warning(paste0("Length of the provided future occurrences is ",length(c(occurrence)),
" while length of forecasting horizon is ",h,".\n",
"Where should we plug in the future occurences anyway?\n",
"Switching to occurrence='fixed'."),call.=FALSE);
occurrence <- "f";
}
if(any(occurrence!=0 & occurrence!=1)){
warning(paste0("Parameter 'occurrence' should contain only zeroes and ones.\n",
"Converting to appropriate vector."),call.=FALSE);
occurrence <- (occurrence!=0)*1;
}
}
else{
obsNonzero <- sum((yInSample!=0)*1);
occurrence <- occurrence[1];
if(all(occurrence!=c("n","a","f","g","o","i","d",
"none","auto","fixed","general","odds-ratio","inverse-odds-ratio","direct"))){
warning(paste0("Strange type of occurrence model defined: '",occurrence,"'. Switching to 'fixed'."),
call.=FALSE);
occurrence <- "f";
}
occurrence <- substring(occurrence,1,1);
environment(intermittentParametersSetter) <- environment();
intermittentParametersSetter(occurrence,ParentEnvironment=environment());
}
# If the data is not occurrence, let's assume that the parameter was switched unintentionally.
if(all(ot==1) & all(occurrence!=c("n","p","provided"))){
occurrence <- "n";
occurrenceModelProvided <- FALSE;
}
##### Define regressors #####
if(!any(regressors==c("use","select","u","s"))){
warning("Wrong type of regressors parameter. Changing to 'select'.", call.=FALSE);
regressors <- "select";
}
regressors <- substr(regressors[1],1,1);
if(is.null(xreg)){
regressors <- "u";
}
##### Return values to previous environment #####
assign("h",h,ParentEnvironment);
assign("holdout",holdout,ParentEnvironment);
assign("silentText",silentText,ParentEnvironment);
assign("silentGraph",silentGraph,ParentEnvironment);
assign("silentLegend",silentLegend,ParentEnvironment);
assign("bounds",bounds,ParentEnvironment);
assign("FI",FI,ParentEnvironment);
assign("obsInSample",obsInSample,ParentEnvironment);
assign("obsAll",obsAll,ParentEnvironment);
assign("obsNonzero",obsNonzero,ParentEnvironment);
assign("initialValue",initialValue,ParentEnvironment);
assign("initialType",initialType,ParentEnvironment);
assign("ic",ic,ParentEnvironment);
assign("loss",loss,ParentEnvironment);
assign("multisteps",multisteps,ParentEnvironment);
assign("interval",interval,ParentEnvironment);
assign("intervalType",intervalType,ParentEnvironment);
assign("occurrence",occurrence,ParentEnvironment);
assign("yInSample",yInSample,ParentEnvironment);
assign("y",y,ParentEnvironment);
assign("dataFreq",dataFreq,ParentEnvironment);
assign("dataStart",dataStart,ParentEnvironment);
assign("yForecastStart",yForecastStart,ParentEnvironment);
assign("regressors",regressors,ParentEnvironment);
}
##### *ssFitter function* #####
ssFitter <- function(...){
ellipsis <- list(...);
ParentEnvironment <- ellipsis[['ParentEnvironment']];
fitting <- fitterwrap(matvt, matF, matw, yInSample, vecg,
lagsModel, Etype, Ttype, Stype, initialType,
matxt, matat, matFX, vecgX, ot);
statesNames <- colnames(matvt);
matvt <- ts(fitting$matvt,start=(time(y)[1] - deltat(y)*lagsModelMax),frequency=dataFreq);
colnames(matvt) <- statesNames;
yFitted <- ts(fitting$yfit,start=dataStart,frequency=dataFreq);
errors <- ts(fitting$errors,start=dataStart,frequency=dataFreq);
if(any(is.nan(matvt[,1]))){
matvt[is.nan(matvt[,1]),1] <- 0;
warning(paste0("Something went wrong with the model ",model,", NaNs were produced.\n",
"This could happen if you used multiplicative model on non-positive data. ",
"Please, use a different model."),
call.=FALSE);
}
if(Etype=="M" & any(matvt[,1]<0)){
matvt[matvt[,1]<0,1] <- 0.001;
warning(paste0("Negative values produced in the level of state vector of model ",model,".\n",
"We had to substitute them by low values. Please, use a different model."),
call.=FALSE);
}
if(!is.null(xreg)){
# Write down the matat and copy values for the holdout
matat[1:nrow(fitting$matat),] <- fitting$matat;
}
if(interval && h>0){
errors.mat <- ts(errorerwrap(matvt, matF, matw, yInSample,
h, Etype, Ttype, Stype, lagsModel,
matxt, matat, matFX, ot),
start=dataStart,frequency=dataFreq);
colnames(errors.mat) <- paste0("Error",c(1:h));
}
else{
errors.mat <- NA;
}
# Correct the fitted values for the cases of intermittent models.
yFitted[] <- yFitted * pFitted;
assign("matvt",matvt,ParentEnvironment);
assign("yFitted",yFitted,ParentEnvironment);
assign("matat",matat,ParentEnvironment);
assign("errors.mat",errors.mat,ParentEnvironment);
assign("errors",errors,ParentEnvironment);
}
##### *State space interval* #####
ssIntervals <- function(errors, ev=median(errors), level=0.95, intervalType=c("a","p","l","sp","np"), df=NULL,
measurement=NULL, transition=NULL, persistence=NULL, s2=NULL,
lagsModel=NULL, states=NULL, cumulative=FALSE, loss="MSE",
yForecast=rep(0,ncol(errors)), Etype="A", Ttype="N", Stype="N", s2g=NULL,
probability=1){
# Function constructs interval based on the provided random variable.
# If errors is a matrix, then it is assumed that each column has a variable that needs an interval.
# based on errors the horison is estimated as ncol(errors)
# Make a correction for the cases, when likelihood is used for the estimation
if(loss=="likelihood"){
loss <- "MSE";
}
matrixpower <- function(A,n){
if(n==0){
return(diag(nrow(A)));
}
else if(n==1){
return(A);
}
else if(n>1){
return(A %*% matrixpower(A, n-1));
}
}
hsmN <- gamma(0.75)*pi^(-0.5)*2^(-0.75);
intervalType <- intervalType[1]
# Check the provided type of interval
if(is.logical(intervalType)){
if(intervalType){
intervalType <- "p";
}
else{
intervalType <- "none";
}
}
if(all(intervalType!=c("a","p","l","s","n","a","sp","np","none","parametric","likelihood",
"semiparametric","nonparametric","asymmetric"))){
stop(paste0("What do you mean by 'intervalType=",intervalType,"'? I can't work with this!"),
call.=FALSE);
}
if(intervalType=="none"){
intervalType <- "n";
}
else if(intervalType=="parametric"){
intervalType <- "p";
}
# If it is likelihood, then it is "parametric", just uses a differen number of degrees of freedom
else if(any(intervalType==c("likelihood","l"))){
intervalType <- "p";
}
else if(intervalType=="semiparametric"){
intervalType <- "sp";
}
else if(intervalType=="nonparametric"){
intervalType <- "np";
}
if(intervalType=="p"){
if(any(is.null(measurement),is.null(transition),is.null(persistence),is.null(s2),is.null(lagsModel))){
stop(paste0("measurement, transition, persistence, s2 and lagsModel ",
"need to be provided in order to construct the parametric interval!"),
call.=FALSE);
}
if(any(!is.matrix(measurement),!is.matrix(transition),!is.matrix(persistence))){
stop(paste0("measurement, transition and persistence must me matrices. ",
"Can't do stuff with what you've provided."),
call.=FALSE);
}
}
# Function allows to estimate the coefficients of the simple quantile regression.
# Used in interval construction.
quantfunc <- function(A){
ee[] <- ye - (A[1]*xe^A[2]);
return((1-quant)*sum(abs(ee[ee<0]))+quant*sum(abs(ee[ee>=0])));
}
# Function returns quantiles of Bernoulli-lognormal cumulative distribution for a predefined parameters
qlnormBin <- function(probability, level=0.95, meanVec=0, sdVec=1, Etype="A"){
levelResidual <- (level - (1-probability)) / probability
lowerquant <- upperquant <- rep(0,length(sdVec));
positiveLevels <- levelResidual>0;
if(any(positiveLevels)){
# If this is Laplace or S, then get b values
if(loss=="MAE"){
sdVec <- sqrt(sdVec/2);
}
else if(loss=="HAM"){
sdVec <- (sdVec/120)^0.25;
}
# Produce lower quantiles if the probability is still lower than the lower P
if(Etype=="A" | all(Etype=="M",all((1-probability) < (1-level)/2))){
if(Etype=="M"){
if(loss=="MAE"){
lowerquant[positiveLevels] <- exp(qlaplace((1-levelResidual[positiveLevels])/2,
meanVec,sdVec));
}
else if(loss=="HAM"){
lowerquant[positiveLevels] <- exp(qs((1-levelResidual[positiveLevels])/2,
meanVec,sdVec));
}
else{
lowerquant[positiveLevels] <- qlnorm((1-levelResidual[positiveLevels])/2,
meanVec,sdVec);
}
}
else{
if(loss=="MAE"){
lowerquant[positiveLevels] <- qlaplace((1-levelResidual[positiveLevels])/2,
meanVec,sdVec);
}
else if(loss=="HAM"){
lowerquant[positiveLevels] <- qs((1-levelResidual[positiveLevels])/2,
meanVec,sdVec);
}
else{
lowerquant[positiveLevels] <- qnorm((1-levelResidual[positiveLevels])/2,
meanVec,sdVec);
}
}
levelNew <- (1+levelResidual[positiveLevels])/2;
}
else{
levelNew <- levelResidual[positiveLevels];
}
# Produce upper quantiles
if(Etype=="M"){
if(loss=="MAE"){
upperquant[positiveLevels] <- exp(qlaplace(levelNew,meanVec,sdVec));
}
else if(loss=="HAM"){
upperquant[positiveLevels] <- exp(qs(levelNew,meanVec,sdVec));
}
else{
upperquant[positiveLevels] <- qlnorm(levelNew,meanVec,sdVec);
}
}
else{
if(loss=="MAE"){
upperquant[positiveLevels] <- qlaplace(levelNew,meanVec,sdVec);
}
else if(loss=="HAM"){
upperquant[positiveLevels] <- qs(levelNew,meanVec,sdVec);
}
else{
upperquant[positiveLevels] <- qnorm(levelNew,meanVec,sdVec);
}
}
}
return(list(lower=lowerquant,upper=upperquant));
}
if(loss=="MAE"){
upperquant <- qlaplace((1+level)/2,0,1);
lowerquant <- qlaplace((1-level)/2,0,1);
}
else if(loss=="HAM"){
upperquant <- qs((1+level)/2,0,1);
lowerquant <- qs((1-level)/2,0,1);
}
else{
#if(loss=="MSE")
# If degrees of freedom are provided, use Student's distribution. Otherwise stick with normal.
if(is.null(df)){
upperquant <- qnorm((1+level)/2,0,1);
lowerquant <- qnorm((1-level)/2,0,1);
}
else{
if(df>0){
upperquant <- qt((1+level)/2,df=df);
lowerquant <- qt((1-level)/2,df=df);
}
else{
upperquant <- sqrt(1/((1-level)/2));
lowerquant <- -upperquant;
}
}
}
##### If they want us to produce several steps ahead #####
if(is.matrix(errors) | is.data.frame(errors)){
if(!cumulative){
nVariables <- ncol(errors);
}
else{
nVariables <- 1;
}
obs <- nrow(errors);
if(length(ev)==1){
ev <- rep(ev,nVariables);
}
upper <- rep(NA,nVariables);
lower <- rep(NA,nVariables);
#### Asymmetric interval using HM ####
if(intervalType=="a"){
if(!cumulative){
for(i in 1:nVariables){
upper[i] <- ev[i] + upperquant / hsmN^2 * Re(hm(errors[,i],ev[i]))^2;
lower[i] <- ev[i] + lowerquant / hsmN^2 * Im(hm(errors[,i],ev[i]))^2;
}
}
else{
upper <- ev + upperquant / hsmN^2 * Re(hm(rowSums(errors),sum(ev)))^2;
lower <- ev + lowerquant / hsmN^2 * Im(hm(rowSums(errors),sum(ev)))^2;
}
if(Etype=="M"){
upper <- yForecast*(1 + upper);
lower <- yForecast*(1 + lower);
}
varVec <- NULL;
}
#### Semiparametric interval using the variance of errors ####
else if(intervalType=="sp"){
if(Etype=="M"){
errors[errors < -1] <- -0.999;
if(!cumulative){
varVec <- colSums(log(1+errors)^2,na.rm=T)/df;
if(any(probability!=1)){
quants <- qlnormBin(probability, level=level, meanVec=log(yForecast),
sdVec=sqrt(varVec), Etype="M");
upper <- quants$upper;
lower <- quants$lower;
}
else{
if(loss=="MAE"){
varVec <- sqrt(varVec/2);
upper <- exp(qlaplace((1+level)/2,0,varVec));
lower <- exp(qlaplace((1-level)/2,0,varVec));
}
else if(loss=="HAM"){
varVec <- (varVec/120)^0.25;
upper <- exp(qs((1+level)/2,0,varVec));
lower <- exp(qs((1-level)/2,0,varVec));
}
else{
upper <- qlnorm((1+level)/2,rep(0,nVariables),sqrt(varVec));
lower <- qlnorm((1-level)/2,rep(0,nVariables),sqrt(varVec));
}
}
upper <- yForecast*upper;
lower <- yForecast*lower;
}
else{
#This is wrong. And there's not way to make it right.
varVec <- sum(rowSums(log(1+errors))^2,na.rm=T)/df;
if(any(probability!=1)){
quants <- qlnormBin(probability, level=level, meanVec=log(sum(yForecast)),
sdVec=sqrt(varVec), Etype="M");
upper <- quants$upper;
lower <- quants$lower;
}
else{
if(loss=="MAE"){
varVec <- sqrt(varVec/2);
upper <- exp(qlaplace((1+level)/2,0,varVec));
lower <- exp(qlaplace((1-level)/2,0,varVec));
}
else if(loss=="HAM"){
varVec <- (varVec/120)^0.25;
upper <- exp(qs((1+level)/2,0,varVec));
lower <- exp(qs((1-level)/2,0,varVec));
}
else{
upper <- qlnorm((1+level)/2,rep(0,nVariables),sqrt(varVec));
lower <- qlnorm((1-level)/2,rep(0,nVariables),sqrt(varVec));
}
}
upper <- sum(yForecast)*upper;
lower <- sum(yForecast)*lower;
}
}
else{
if(!cumulative){
errors <- errors - matrix(ev,nrow=obs,ncol=nVariables,byrow=T);
varVec <- colSums(errors^2,na.rm=T)/df;
if(any(probability!=1)){
quants <- qlnormBin(probability, level=level, meanVec=ev, sdVec=sqrt(varVec), Etype="A");
upper <- ev + quants$upper;
lower <- ev + quants$lower;
}
else{
if(loss=="MAE"){
# s^2 = 2 b^2 => b^2 = s^2 / 2
varVec <- varVec / 2;
}
else if(loss=="HAM"){
# s^2 = 120 b^4 => b^4 = s^2 / 120
# S(mu, b) = S(mu, 1) * 50^2
varVec <- varVec/120;
}
upper <- ev + upperquant * sqrt(varVec);
lower <- ev + lowerquant * sqrt(varVec);
}
}
else{
errors <- errors - matrix(ev,nrow=obs,ncol=ncol(errors),byrow=T);
varVec <- sum(rowSums(errors,na.rm=T)^2,na.rm=T)/df;
if(any(probability!=1)){
quants <- qlnormBin(probability, level=level, meanVec=sum(ev), sdVec=sqrt(varVec), Etype="A");
upper <- sum(ev) + quants$upper;
lower <- sum(ev) + quants$lower;
}
else{
if(loss=="MAE"){
# s^2 = 2 b^2 => b^2 = s^2 / 2
varVec <- varVec / 2;
}
else if(loss=="HAM"){
# s^2 = 120 b^4 => b^4 = s^2 / 120
# S(mu, b) = S(mu, 1) * 50^2
varVec <- varVec/120;
}
upper <- sum(ev) + upperquant * sqrt(varVec);
lower <- sum(ev) + lowerquant * sqrt(varVec);
}
}
}
}
#### Nonparametric interval using Taylor and Bunn, 1999 ####
else if(intervalType=="np"){
nonNAobs <- apply(!is.na(errors),1,all);
ye <- errors[nonNAobs,];
if(Etype=="M"){
ye <- 1 + ye;
}
# Define the correct bounds for the intermittent model
levelResidual <- (level - (1-probability)) / probability;
lower <- upper <- rep(0,length(yForecast));
if(!cumulative){
ee <- ye;
xe <- matrix(c(1:nVariables),nrow=sum(nonNAobs),ncol=nVariables,byrow=TRUE);
if(Etype=="A" | all(Etype=="M",all((1-probability) < (1-level)/2))){
A <- rep(1,2);
quant <- (1-levelResidual)/2;
A <- nlminb(A,quantfunc)$par;
lower <- A[1]*c(1:nVariables)^A[2];
levelNew <- (1+levelResidual)/2;
}
else{
levelNew <- levelResidual;
}
A <- rep(1,2);
quant <- levelNew;
A <- nlminb(A,quantfunc)$par;
upper <- A[1]*c(1:nVariables)^A[2];
if(Etype=="M"){
upper <- yForecast * upper;
lower <- yForecast * lower;
}
}
else{
if(Etype=="A" | all(Etype=="M",all((1-probability) < (1-level)/2))){
#This is wrong. And there's no way to make it right.
lower <- quantile(rowSums(ye),(1-levelResidual)/2);
levelNew <- (1+levelResidual)/2;
}
else{
levelNew <- levelResidual;
}
upper <- quantile(rowSums(ye),levelNew);
}
varVec <- NULL;
}
#### Parametric interval ####
else if(intervalType=="p"){
h <- length(yForecast);
# Vector of final variances
varVec <- rep(NA,h);
#### Pure Multiplicative models ####
if(Etype=="M"){
# This is just an approximation of the true interval
covarMat <- covarAnal(lagsModel, h, measurement, transition, persistence, s2);
### Cumulative variance is different.
if(cumulative){
varVec <- sum(covarMat);
varVec <- log(exp(varVec / h) * h);
if(any(probability!=1)){
quants <- qlnormBin(probability, level=level, meanVec=log(sum(yForecast)),
sdVec=sqrt(varVec), Etype="M");
upper <- quants$upper;
lower <- quants$lower;
}
else{
if(loss=="MAE"){
varVec <- sqrt(varVec / 2);
upper <- exp(qlaplace((1+level)/2,0,varVec));
lower <- exp(qlaplace((1-level)/2,0,varVec));
}
else if(loss=="HAM"){
varVec <- (varVec/120)^0.25;
upper <- exp(qs((1+level)/2,0,varVec));
lower <- exp(qs((1-level)/2,0,varVec));
}
else{
# Produce quantiles for log-normal dist with the specified variance
upper <- qlnorm((1+level)/2,0,sqrt(varVec));
lower <- qlnorm((1-level)/2,0,sqrt(varVec));
}
upper <- sum(yForecast)*upper;
lower <- sum(yForecast)*lower;
}
}
else{
varVec <- diag(covarMat);
if(any(probability!=1)){
quants <- qlnormBin(probability, level=level, meanVec=log(yForecast),
sdVec=sqrt(varVec), Etype="M");
upper <- quants$upper;
lower <- quants$lower;
}
else{
if(loss=="MAE"){
# s^2 = 2 b^2 => b = sqrt(s^2 / 2)
varVec <- sqrt(varVec / 2);
upper <- exp(qlaplace((1+level)/2,0,varVec));
lower <- exp(qlaplace((1-level)/2,0,varVec));
}
else if(loss=="HAM"){
# s^2 = 120 b^4 => b^4 = s^2 / 120
# S(mu, b) = S(mu, 1) * 50^2
varVec <- (varVec/120)^0.25;
upper <- exp(qs((1+level)/2,0,varVec));
lower <- exp(qs((1-level)/2,0,varVec));
}
else{
# Produce quantiles for log-normal dist with the specified variance
upper <- qlnorm((1+level)/2,0,sqrt(varVec));
lower <- qlnorm((1-level)/2,0,sqrt(varVec));
}
upper <- yForecast*upper;
lower <- yForecast*lower;
}
}
}
#### Multiplicative error and additive trend / seasonality
# else if(Etype=="M" & all(c(Ttype,Stype)!="M") & all(c(Ttype,Stype)!="N")){
# }
#### Pure Additive models ####
else{
covarMat <- covarAnal(lagsModel, h, measurement, transition, persistence, s2);
### Cumulative variance is a sum of all the elements of the matrix
if(cumulative){
varVec <- sum(covarMat);
}
else{
varVec <- diag(covarMat);
}
if(any(probability!=1)){
# Take intermittent data into account
quants <- qlnormBin(probability, level=level, meanVec=rep(0,length(varVec)),
sdVec=sqrt(varVec), Etype="A");
upper <- quants$upper;
lower <- quants$lower;
}
else{
if(loss=="MAE"){
# s^2 = 2 b^2 => b^2 = s^2 / 2
varVec <- varVec / 2;
}
else if(loss=="HAM"){
# s^2 = 120 b^4 => b^4 = s^2 / 120
# S(mu, b) = S(mu, 1) * b^2
varVec <- varVec/120;
}
upper <- upperquant * sqrt(varVec);
lower <- lowerquant * sqrt(varVec);
}
}
}
}
##### If we were asked to produce 1 value #####
else if(is.numeric(errors) & length(errors)>1 & !is.array(errors)){
if(length(ev)>1){
stop("Provided expected value doesn't correspond to the dimension of errors.", call.=FALSE);
}
if(intervalType=="a"){
upper <- ev + upperquant / hsmN^2 * Re(hm(errors,ev))^2;
lower <- ev + lowerquant / hsmN^2 * Im(hm(errors,ev))^2;
}
else if(any(intervalType==c("sp","p"))){
if(Etype=="M"){
if(any(probability!=1)){
quants <- qlnormBin(probability, level=level, meanVec=0, sdVec=sqrt(s2), Etype="M");
upper <- quants$upper;
lower <- quants$lower;
}
else{
if(loss=="MAE"){
# s^2 = 2 b^2 => b = sqrt(s^2 / 2)
s2 <- sqrt(s2 / 2);
upper <- exp(qlaplace((1+level)/2,0,s2));
lower <- exp(qlaplace((1-level)/2,0,s2));
}
else if(loss=="HAM"){
# s^2 = 120 b^4 => b^4 = s^2 / 120
# S(mu, b) = S(mu, 1) * 50^2
s2 <- (s2/120)^0.25;
upper <- exp(qs((1+level)/2,0,s2));
lower <- exp(qs((1-level)/2,0,s2));
}
else{
upper <- qlnorm((1+level)/2,0,sqrt(s2));
lower <- qlnorm((1-level)/2,0,sqrt(s2));
}
upper <- yForecast*upper;
lower <- yForecast*lower;
}
}
else{
if(any(probability!=1)){
quants <- qlnormBin(probability, level=level, meanVec=ev, sdVec=sqrt(s2), Etype="A");
upper <- quants$upper;
lower <- quants$lower;
}
else{
if(loss=="MAE"){
# s^2 = 2 b^2 => b^2 = s^2 / 2
s2 <- s2 / 2;
}
else if(loss=="HAM"){
# s^2 = 120 b^4 => b^4 = s^2 / 120
# S(mu, b) = S(mu, 1) * 50^2
s2 <- s2/120;
}
upper <- ev + upperquant * sqrt(s2);
lower <- ev + lowerquant * sqrt(s2);
}
}
}
else if(intervalType=="np"){
if(Etype=="M"){
errors <- errors + 1;
}
upper <- quantile(errors,(1+level)/2);
lower <- quantile(errors,(1-level)/2);
}
varVec <- NULL;
}
else{
stop("The provided data is not either vector or matrix. Can't do anything with it!", call.=FALSE);
}
return(list(upper=upper,lower=lower));
}
##### *Forecaster of state space functions* #####
ssForecaster <- function(...){
ellipsis <- list(...);
ParentEnvironment <- ellipsis[['ParentEnvironment']];
if(intervalType!="l" && obsInSample > nParam){
obsDF <- obsInSample - nParam;
}
else{
obsDF <- obsInSample;
}
if(!rounded){
s2 <- as.vector(sum((errors*ot)^2)/obsDF);
# If error is additive, s2g is just 1
if(Etype=="A"){
s2g <- 1;
}
else{
s2g <- log(1 + vecg %*% as.vector(errors*ot)) %*% t(log(1 + vecg %*%
as.vector(errors*ot)))/obsDF;
}
}
if(h>0){
yForecast <- ts(c(forecasterwrap(matvt[(obsInSample+1):(obsInSample+lagsModelMax),,drop=FALSE],
matF, matw, h, Etype, Ttype, Stype, lagsModel,
matxt[(obsAll-h+1):(obsAll),,drop=FALSE],
matat[(obsAll-h+1):(obsAll),,drop=FALSE], matFX)),
start=yForecastStart,frequency=dataFreq);
if(any(is.nan(yForecast))){
warning(paste0("NaNs were produced in the forecast.\n",
"This could happen if you used multiplicative model on non-positive data. ",
"Please, use a different model."),
call.=FALSE);
yForecast[] <- 0;
}
if(Etype=="M" & any(yForecast<0)){
warning(paste0("Negative values produced in forecast. ",
"This does not make any sense for model with multiplicative error.\n",
"Please, use another model."),
call.=FALSE);
if(interval){
warning("And don't expect anything reasonable from the prediction interval!",
call.=FALSE);
}
}
# Write down the forecasting interval
if(interval){
if(h==1){
errors.x <- as.vector(errors);
ev <- median(errors);
}
else{
errors.x <- errors.mat;
ev <- apply(errors.mat,2,median,na.rm=TRUE);
}
if(intervalType!="a"){
ev <- 0;
}
# We don't simulate pure additive models, pure multiplicative and
# additive models with multiplicative error,
# because they can be approximated by the pure additive ones
if(any(intervalType==c("p","l"))){
if(all(c(Etype,Stype,Ttype)!="M") |
all(c(Etype,Stype,Ttype)!="A") |
(all(Etype=="M",any(Ttype==c("A","N")),any(Stype==c("A","N"))) & s2<0.1)){
simulateIntervals <- FALSE;
}
else{
simulateIntervals <- TRUE;
}
}
else{
simulateIntervals <- FALSE;
}
# It is not possible to produce parametric / semi / non interval for cumulative values
# of multiplicative model. So we use simulations instead.
# if(Etype=="M"){
# simulateIntervals <- TRUE;
# }
if(simulateIntervals){
nSamples <- 100000;
matg <- matrix(vecg,nComponents,nSamples);
arrvt <- array(NA,c(h+lagsModelMax,nComponents,nSamples));
arrvt[1:lagsModelMax,,] <- rep(matvt[obsInSample+(1:lagsModelMax),],nSamples);
materrors <- matrix(rnorm(h*nSamples,0,sqrt(s2)),h,nSamples);
if(Etype=="M"){
materrors <- exp(materrors) - 1;
}
if(all(occurrence!=c("n","p"))){
matot <- matrix(rbinom(h*nSamples,1,pForecast),h,nSamples);
}
else{
matot <- matrix(1,h,nSamples);
}
ySimulated <- simulatorwrap(arrvt,materrors,matot,array(matF,c(dim(matF),nSamples)),matw,matg,
Etype,Ttype,Stype,lagsModel)$matyt;
if(!is.null(xreg)){
yForecastExo <- (c(yForecast) -
forecasterwrap(matrix(matvt[(obsInSample+1):(obsInSample+lagsModelMax),],
nrow=lagsModelMax),
matF, matw, h, Etype, Ttype, Stype, lagsModel,
matrix(rep(1,h),ncol=1), matrix(rep(0,h),ncol=1),
matrix(1,1,1)));
}
else{
yForecastExo <- rep(0,h);
}
if(Etype=="M"){
yForecast[] <- apply(ySimulated, 1, mean);
}
if(rounded){
ySimulated <- ceiling(ySimulated + matrix(yForecastExo,nrow=h,ncol=nSamples));
quantileType <- 1;
for(i in 1:h){
yForecast[i] <- median(ySimulated[i,ySimulated[i,]!=0]);
}
# NA means that there were no non-zero demands
yForecast[is.na(yForecast)] <- 0;
}
else{
ySimulated <- ySimulated + matrix(yForecastExo,nrow=h,ncol=nSamples);
quantileType <- 7;
}
yForecast[] <- yForecast + yForecastExo;
yForecast[] <- pForecast * yForecast;
if(cumulative){
yForecast <- ts(sum(yForecast),start=yForecastStart,frequency=dataFreq);
yLower <- ts(quantile(colSums(ySimulated,na.rm=T),(1-level)/2,type=quantileType),
start=yForecastStart,frequency=dataFreq);
yUpper <- ts(quantile(colSums(ySimulated,na.rm=T),(1+level)/2,type=quantileType),
start=yForecastStart,frequency=dataFreq);
}
else{
# yForecast <- ts(yForecast,start=yForecastStart,frequency=dataFreq);
yLower <- ts(apply(ySimulated,1,quantile,(1-level)/2,na.rm=T,type=quantileType) +
yForecastExo,
start=yForecastStart,frequency=dataFreq);
yUpper <- ts(apply(ySimulated,1,quantile,(1+level)/2,na.rm=T,type=quantileType) +
yForecastExo,
start=yForecastStart,frequency=dataFreq);
}
}
else{
quantvalues <- ssIntervals(errors.x, ev=ev, level=level, intervalType=intervalType,
df=obsDF,
measurement=matw, transition=matF, persistence=vecg, s2=s2,
lagsModel=lagsModel, states=matvt[(obsInSample-lagsModelMax+1):obsInSample,],
cumulative=cumulative, loss=loss,
yForecast=yForecast, Etype=Etype, Ttype=Ttype, Stype=Stype, s2g=s2g,
probability=pForecast);
# if(!(intervalType=="sp" & Etype=="M")){
yForecast[] <- c(pForecast) * c(yForecast);
# }
if(cumulative){
yForecast <- ts(sum(yForecast),start=yForecastStart,frequency=dataFreq);
}
if(Etype=="A"){
yLower <- ts(c(yForecast) + quantvalues$lower,start=yForecastStart,frequency=dataFreq);
yUpper <- ts(c(yForecast) + quantvalues$upper,start=yForecastStart,frequency=dataFreq);
}
else{
# if(any(intervalType==c("np","sp","a"))){
# quantvalues$upper <- quantvalues$upper * yForecast;
# quantvalues$lower <- quantvalues$lower * yForecast;
# }
yLower <- ts(quantvalues$lower,start=yForecastStart,frequency=dataFreq);
yUpper <- ts(quantvalues$upper,start=yForecastStart,frequency=dataFreq);
}
if(rounded){
yLower <- ceiling(yLower);
yUpper <- ceiling(yUpper);
}
}
}
else{
yLower <- NA;
yUpper <- NA;
if(rounded){
yForecast[] <- ceiling(yForecast);
}
yForecast[] <- pForecast*yForecast;
if(cumulative){
yForecast <- ts(sum(yForecast),start=yForecastStart,frequency=dataFreq);
}
# else{
# yForecast <- ts(yForecast,start=yForecastStart,frequency=dataFreq);
# }
}
}
else{
yLower <- NA;
yUpper <- NA;
yForecast <- ts(NA,start=yForecastStart,frequency=dataFreq);
}
if(any(is.na(yFitted),all(is.na(yForecast),h>0))){
warning("Something went wrong during the optimisation and NAs were produced!",call.=FALSE);
warning("Please check the input and report this error to the maintainer if it persists.",call.=FALSE);
}
assign("s2",s2,ParentEnvironment);
assign("yForecast",yForecast,ParentEnvironment);
assign("yLower",yLower,ParentEnvironment);
assign("yUpper",yUpper,ParentEnvironment);
}
##### *Check and initialisation of xreg* #####
ssXreg <- function(y, Etype="A", xreg=NULL, updateX=FALSE, ot=NULL,
persistenceX=NULL, transitionX=NULL, initialX=NULL,
obsInSample, obsAll, obsStates, lagsModelMax=1, h=1, regressors="u", silent=FALSE,
allowMultiplicative=FALSE){
# The function does general checks needed for exogenouse variables and returns the list of
# necessary parameters
if(!is.null(xreg)){
xreg <- as.matrix(xreg);
if(any(is.na(xreg))){
warning(paste0("The exogenous variables contain NAs! ",
"This may lead to problems during estimation and in forecasting.",
"\nSubstituting them with 0."),
call.=FALSE);
xreg[is.na(xreg)] <- 0;
}
if(!is.null(dim(xreg))){
if(ncol(xreg)==1){
xreg <- as.vector(xreg);
}
if(length(dim(xreg))>2){
stop(paste0("Sorry, but we don't deal with multidimensional arrays as exogenous variables here.\n",
"Pleas think of your behaviour and come back with matrix!"),call.=FALSE);
}
}
##### The case with vectors and ts objects, but not matrices
if(is.vector(xreg) | (is.ts(xreg) & !is.matrix(xreg))){
# Check if xreg contains something meaningful
if(is.null(initialX)){
if(all(xreg[1:obsInSample]==xreg[1])){
warning(paste0("The exogenous variable has no variability. ",
"Cannot do anything with that, so dropping out xreg."),
call.=FALSE);
xreg <- NULL;
}
}
if(!is.null(xreg)){
if(length(xreg) < obsAll){
warning("xreg did not contain values for the holdout, so we had to predict missing values.",
call.=FALSE);
# If this is a binary variable, use iss function.
if(all((xreg==0) | (xreg==1))){
xregForecast <- oes(xreg, model="MNN", h=obsAll-length(xreg),
occurrence="o", ic="AIC")$forecast;
}
else{
xregForecast <- es(xreg, h=obsAll-length(xreg), ic="AICc",silent=TRUE)$forecast;
}
xreg <- c(as.vector(xreg),as.vector(xregForecast));
}
else if(length(xreg) > obsAll){
warning("xreg contained too many observations, so we had to cut off some of them.",
call.=FALSE);
xreg <- xreg[1:obsAll];
}
if(all(y[1:obsInSample]==xreg[1:obsInSample])){
warning(paste0("The exogenous variable and the forecasted data are exactly the same. ",
"What's the point of such a regression?"),
call.=FALSE);
xreg <- NULL;
}
# Number of exogenous variables
nExovars <- 1;
# Define matrix w for exogenous variables
matxt <- matrix(xreg,ncol=1);
# Define the second matat to fill in the coefs of the exogenous vars
matatMultiplicative <- matat <- matrix(NA,obsStates,1);
# Fill in the initial values for exogenous coefs using OLS
if(is.null(initialX)){
if(Etype=="M"){
matat[1:lagsModelMax,] <- cov(log(y[1:obsInSample][ot==1]),
xreg[1:obsInSample][ot==1])/var(xreg[1:obsInSample][ot==1]);
matatMultiplicative[1:lagsModelMax,] <- matat[1:lagsModelMax,];
}
else{
matat[1:lagsModelMax,] <- cov(y[1:obsInSample][ot==1],
xreg[1:obsInSample][ot==1])/var(xreg[1:obsInSample][ot==1]);
}
matat[] <- matat[1,]
# If Etype=="Z" or "C", estimate multiplicative stuff.
if(allowMultiplicative & all(Etype!=c("M","A"))){
matatMultiplicative[1:lagsModelMax,] <- cov(log(y[1:obsInSample][ot==1]),
xreg[1:obsInSample][ot==1])/var(xreg[1:obsInSample][ot==1]);
}
}
if(is.null(names(xreg))){
colnames(matxt) <- colnames(matat) <- colnames(matatMultiplicative) <- "x";
}
else{
xregNames <- gsub(" ", "_", names(xreg), fixed = TRUE);
colnames(matxt) <- colnames(matat) <- colnames(matatMultiplicative) <- xregNames
}
}
if(!is.null(xreg)){
xreg <- as.matrix(xreg);
}
}
##### The case with matrices and data frames
else if(is.matrix(xreg) | is.data.frame(xreg)){
if(is.data.frame(xreg)){
xreg <- as.matrix(xreg);
}
nExovars <- ncol(xreg);
if(nrow(xreg) < obsAll){
warning("xreg did not contain values for the holdout, so we had to predict missing values.",
call.=FALSE);
xregForecast <- matrix(NA,nrow=obsAll-nrow(xreg),ncol=nExovars);
if(!silent){
message("Producing forecasts for xreg variable...");
}
for(j in 1:nExovars){
if(!silent){
cat(paste0(rep("\b",nchar(round((j-1)/nExovars,2)*100)+1),collapse=""));
cat(paste0(round(j/nExovars,2)*100,"%"));
}
if(all((xreg[,j]==0) | (xreg[,j]==1))){
xregForecast[,j] <- oes(xreg[,j], model="MNN", h=obsAll-nrow(xreg), occurrence="o",ic="AIC")$forecast;
}
else{
xregForecast[,j] <- es(xreg[,j], h=obsAll-nrow(xreg), ic="AICc")$forecast;
}
}
xreg <- rbind(xreg,xregForecast);
if(!silent){
cat("\b\b\b\bDone!\n");
}
}
else if(nrow(xreg) > obsAll){
warning("xreg contained too many observations, so we had to cut off some of them.",
call.=FALSE);
xreg <- xreg[1:obsAll,];
}
xregEqualToData <- apply(xreg[1:obsInSample,]==y[1:obsInSample],2,all);
if(any(xregEqualToData)){
warning(paste0("One of exogenous variables and the forecasted data are exactly the same. ",
"We have dropped it."),
call.=FALSE);
xreg <- matrix(xreg[,!xregEqualToData],nrow=nrow(xreg),ncol=ncol(xreg)-1,
dimnames=list(NULL,colnames(xreg[,!xregEqualToData])));
}
nExovars <- ncol(xreg);
# If initialX is provided, then probably we don't need to check the xreg on variability and multicollinearity
if(is.null(initialX)){
checkvariability <- apply(matrix(xreg[1:obsInSample,][ot==1,]==rep(xreg[ot==1,][1,],
each=sum(ot)),sum(ot),
nExovars),2,all);
if(any(checkvariability)){
if(all(checkvariability)){
warning(paste0("None of exogenous variables has variability. ",
"Cannot do anything with that, so dropping out xreg."),
call.=FALSE);
xreg <- NULL;
nExovars <- 0;
}
else{
warning("Some exogenous variables do not have any variability. Dropping them out.",
call.=FALSE);
xreg <- as.matrix(xreg[,!checkvariability]);
nExovars <- ncol(xreg);
}
}
if(!is.null(xreg)){
# Check for multicollinearity and drop something if there is a perfect one
corMatrix <- cor(xreg);
corCheck <- upper.tri(corMatrix) & abs(corMatrix)>=0.999;
if(any(corCheck)){
removexreg <- unique(which(corCheck,arr.ind=TRUE)[,1]);
if(ncol(xreg)-length(removexreg)>1){
xreg <- xreg[,-removexreg];
}
else{
xreg <- matrix(xreg[,-removexreg],ncol=ncol(xreg)-length(removexreg),
dimnames=list(NULL,c(colnames(xreg)[-removexreg])));
}
nExovars <- ncol(xreg);
warning("Some exogenous variables were perfectly correlated. We've dropped them out.",
call.=FALSE);
}
# Check multiple correlations. This is needed for cases with dummy variables.
# In case with regressors="select" some of the perfectly correlated things,
# will be dropped out automatically.
if(nExovars>2 & (regressors=="u")){
corMatrix <- cor(xreg);
corMulti <- rep(NA,nExovars);
if(det(corMatrix)!=0){
for(i in 1:nExovars){
corMulti[i] <- 1 - det(corMatrix) / det(corMatrix[-i,-i]);
}
if(any(corMulti>=0.999)){
stop(paste0("Some combinations of exogenous variables are perfectly correlated. \n",
"If you use sets of dummy variables, don't forget to drop some of them."),
call.=FALSE);
}
}
else{
stop(paste0("Some combinations of exogenous variables are perfectly correlated. \n",
"If you use sets of dummy variables, don't forget to drop some of them."),
call.=FALSE);
}
}
}
}
if(!is.null(xreg)){
# mat.x is needed for the initial values of coefs estimation using OLS
mat.x <- as.matrix(cbind(rep(1,obsAll),xreg));
# Define the second matat to fill in the coefs of the exogenous vars
matatMultiplicative <- matat <- matrix(NA,obsStates,nExovars);
# Define matrix w for exogenous variables
matxt <- as.matrix(xreg);
# Fill in the initial values for exogenous coefs using OLS
if(is.null(initialX)){
if(Etype=="M"){
matat[1:lagsModelMax,] <- rep(t(solve(t(mat.x[1:obsInSample,][ot==1,]) %*%
mat.x[1:obsInSample,][ot==1,],tol=1e-50) %*%
t(mat.x[1:obsInSample,][ot==1,]) %*%
log(y[1:obsInSample][ot==1]))[2:(nExovars+1)],
each=lagsModelMax);
matatMultiplicative[1:lagsModelMax,] <- matat[1:lagsModelMax,];
}
else{
matat[1:lagsModelMax,] <- rep(t(solve(t(mat.x[1:obsInSample,][ot==1,]) %*%
mat.x[1:obsInSample,][ot==1,],tol=1e-50) %*%
t(mat.x[1:obsInSample,][ot==1,]) %*%
y[1:obsInSample][ot==1])[2:(nExovars+1)],
each=lagsModelMax);
}
matat[-1,] <- rep(matat[1,],each=obsStates-1);
# If Etype=="Z" or "C", estimate multiplicative stuff.
if(allowMultiplicative & all(Etype!=c("M","A"))){
matatMultiplicative[1:lagsModelMax,] <- rep(t(solve(t(mat.x[1:obsInSample,][ot==1,]) %*%
mat.x[1:obsInSample,][ot==1,],tol=1e-50) %*%
t(mat.x[1:obsInSample,][ot==1,]) %*%
log(y[1:obsInSample][ot==1]))[2:(nExovars+1)],
each=lagsModelMax);
}
}
if(is.null(colnames(xreg))){
colnames(matxt) <- colnames(matat) <- colnames(matatMultiplicative) <- paste0("x",c(1:nExovars));
}
else{
xregNames <- gsub(" ", "_", colnames(xreg), fixed = TRUE);
if(regressors=="s" & any(grepl('[^[:alnum:]]', xregNames))){
warning(paste0("There were some special characters in names of ",
"xreg variables. We had to remove them."),call.=FALSE);
xregNames <- gsub("[^[:alnum:]]", "", xregNames);
}
xregDuplicated <- duplicated(colnames(xreg));
if(any(xregDuplicated)){
warning(paste0("Some names of variables are duplicated. ",
"We had to rename them."),call.=FALSE);
xregNames[xregDuplicated] <- paste0("xDuplicated",c(1:sum(xregDuplicated)));
}
colnames(matxt) <- colnames(matat) <- colnames(matatMultiplicative) <- xregNames;
}
}
}
else{
stop("Unknown format of xreg. Should be either vector or matrix. Aborting!",call.=FALSE);
}
xregEstimate <- TRUE;
# Check the provided initialX vector
if(!is.null(initialX)){
if(!is.numeric(initialX) & !is.vector(initialX) & !is.matrix(initialX)){
stop("The initials for exogenous are not a numeric vector or a matrix!", call.=FALSE);
}
else{
if(length(initialX) != nExovars){
stop(paste0("The size of initial vector for exogenous is wrong!\n",
"It should correspond to the number of exogenous variables."), call.=FALSE);
}
else{
matat[1:lagsModelMax,] <- as.vector(rep(initialX,each=lagsModelMax));
initialXEstimate <- FALSE;
}
}
}
else{
initialXEstimate <- TRUE;
}
}
else{
updateX <- FALSE;
}
##### In case we changed xreg to null or if it was like that...
if(is.null(xreg)){
# "1" is needed for the final forecast simplification
nExovars <- 1;
matxt <- matrix(1,obsStates,1);
matatMultiplicative <- matat <- matrix(0,obsStates,1);
matFX <- matrix(1,1,1);
vecgX <- matrix(0,1,1);
xregEstimate <- FALSE;
FXEstimate <- FALSE;
gXEstimate <- FALSE;
initialXEstimate <- FALSE;
}
# Now check transition and persistence of exogenous variables
if(xregEstimate & updateX){
# First - transition matrix
if(!is.null(transitionX)){
if(!is.numeric(transitionX) & !is.vector(transitionX) & !is.matrix(transitionX)){
stop("Transition matrix for exogenous is not a numeric vector or a matrix!", call.=FALSE);
}
else{
if(length(transitionX) != nExovars^2){
stop(paste0("Size of transition matrix for exogenous is wrong!\n",
"It should correspond to the number of exogenous variables."), call.=FALSE);
}
else{
matFX <- matrix(transitionX,nExovars,nExovars);
FXEstimate <- FALSE;
}
}
}
else{
matFX <- diag(nExovars);
FXEstimate <- TRUE;
}
# Now - persistence vector
if(!is.null(persistenceX)){
if(!is.numeric(persistenceX) & !is.vector(persistenceX) & !is.matrix(persistenceX)){
stop("Persistence vector for exogenous is not numeric!", call.=FALSE);
}
else{
if(length(persistenceX) != nExovars){
stop(paste0("Size of persistence vector for exogenous is wrong!\n",
"It should correspond to the number of exogenous variables."), call.=FALSE);
}
else{
vecgX <- matrix(persistenceX,nExovars,1);
gXEstimate <- FALSE;
}
}
}
else{
vecgX <- matrix(0,nExovars,1);
gXEstimate <- TRUE;
}
}
else if(xregEstimate & !updateX){
matFX <- diag(nExovars);
FXEstimate <- FALSE;
vecgX <- matrix(0,nExovars,1);
gXEstimate <- FALSE;
}
if(all(!FXEstimate,!gXEstimate,!initialXEstimate)){
xregEstimate <- FALSE;
}
return(list(nExovars=nExovars, matxt=matxt, matat=matat, matFX=matFX, vecgX=vecgX,
xreg=xreg, xregEstimate=xregEstimate, FXEstimate=FXEstimate,
gXEstimate=gXEstimate, initialXEstimate=initialXEstimate,
matatMultiplicative=matatMultiplicative))
}
##### *Likelihood function* #####
likelihoodFunction <- function(B,yFittedSumLog=0){
#### Concentrated logLikelihood based on B and CF ####
logLikFromCF <- function(B, loss){
if(loss=="likelihood"){
return(-CFValue);
}
if(Etype=="M" && any(loss==c("TMSE","GTMSE","TMAE","GTMAE","THAM","GTHAM",
"GPL","aTMSE","aGTMSE","aGPL"))){
yFittedSumLog <- yFittedSumLog * h;
}
if(all(loss!=c("GTMSE","GTMAE","GTHAM","GPL","aGPL","aGTMSE"))){
CFValue[] <- log(CFValue);
}
if(any(loss==c("MAE","MAEh","MACE","TMAE","GTMAE"))){
return(- (obsInSample*(log(2) + 1 + CFValue) + obsZero) - yFittedSumLog);
}
else if(any(loss==c("HAM","HAMh","CHAM","THAM","GTHAM"))){
#### This is a temporary fix for the oes models... Needs to be done properly!!! ####
return(- 2*(obsInSample*(log(2) + 1 + CFValue) + obsZero) - yFittedSumLog);
}
else if(any(loss==c("GPL","aGPL","aGTMSE"))){
return(- 0.5 *(obsInSample*(h*log(2*pi) + 1 + CFValue) + obsZero) - yFittedSumLog);
}
else{
#if(loss==c("MSE","MSEh","MSCE")) obsNonzero
return(- 0.5 *(obsInSample*(log(2*pi) + 1 + CFValue) + obsZero) - yFittedSumLog);
}
}
CFValue <- CF(B);
if(any(occurrence==c("n","p"))){
return(logLikFromCF(B, loss));
}
else{
# Failsafe for exceptional cases when the probability is equal to zero / one,
# when it should not have been.
if(any(c(1-pFitted[ot==0]==0,pFitted[ot==1]==0))){
# return(-Inf);
ptNew <- pFitted[(pFitted!=0) & (pFitted!=1)];
otNew <- ot[(pFitted!=0) & (pFitted!=1)];
if(length(ptNew)==0){
return(logLikFromCF(B, loss));
}
else{
return(sum(log(ptNew[otNew==1])) + sum(log(1-ptNew[otNew==0]))
+ logLikFromCF(B, loss));
}
}
#Failsafe for cases, when data has no variability when ot==1.
if(CFValue==0){
if(loss=="GPL" | loss=="aGPL"){
return(sum(log(pFitted[ot==1]))*h + sum(log(1-pFitted[ot==0]))*h);
}
else{
return(sum(log(pFitted[ot==1])) + sum(log(1-pFitted[ot==0])));
}
}
if(rounded){
return(sum(log(pFitted[ot==1])) + sum(log(1-pFitted[ot==0])) - CFValue -
obsZero/2*(log(2*pi*B[length(B)]^2)+1));
}
if(loss=="GPL" | loss=="aGPL"){
return(sum(log(pFitted[ot==1]))*h
+ sum(log(1-pFitted[ot==0]))*h
+ logLikFromCF(B, loss));
}
else{
return(sum(log(pFitted[ot==1])) + sum(log(1-pFitted[ot==0]))
+ logLikFromCF(B, loss));
}
}
}
##### *Function calculates ICs* #####
ICFunction <- function(nParam=nParam,nParamOccurrence=nParamOccurrence,
B,Etype=Etype,yFittedSumLog=0){
# Information criteria are calculated with the constant part "log(2*pi*exp(1)*h+log(obs))*obs".
# And it is based on the mean of the sum squared residuals either than sum.
# Hyndman likelihood is: llikelihood <- obs*log(obs*cfObjective)
nParamOverall <- nParam + nParamOccurrence;
llikelihood <- likelihoodFunction(B,yFittedSumLog=yFittedSumLog);
# max here is needed in order to take into account cases with higher
## number of parameters than observations
### AICc and BICc are incorrect in case of non-normal residuals!
if(loss=="GPL"){
coefAIC <- 2*nParamOverall*h - 2*llikelihood;
coefBIC <- log(obsInSample)*nParamOverall*h - 2*llikelihood;
coefAICc <- (2*obsInSample*(nParam*h + (h*(h+1))/2) /
max(obsInSample - nParam - 1 - h,0)
-2*llikelihood);
coefBICc <- (((nParam + (h*(h+1))/2)*
log(obsInSample*h)*obsInSample*h) /
max(obsInSample*h - nParam - (h*(h+1))/2,0)
-2*llikelihood);
}
else{
coefAIC <- 2*nParamOverall - 2*llikelihood;
coefBIC <- log(obsInSample)*nParamOverall - 2*llikelihood;
coefAICc <- coefAIC + 2*nParam*(nParam+1) / max(obsInSample-nParam-1,0);
coefBICc <- (nParam * log(obsInSample) * obsInSample) / (obsInSample - nParam - 1) -2*llikelihood;
}
ICs <- c(coefAIC, coefAICc, coefBIC, coefBICc);
names(ICs) <- c("AIC", "AICc", "BIC", "BICc");
return(list(llikelihood=llikelihood,ICs=ICs));
}
##### *Ouptut printer* #####
ssOutput <- function(timeelapsed, modelname, persistence=NULL, transition=NULL, measurement=NULL,
phi=NULL, ARterms=NULL, MAterms=NULL, constant=NULL, a=NULL, b=NULL, initialType="o",
nParam=NULL, s2=NULL, hadxreg=FALSE, wentwild=FALSE,
loss="MSE", cfObjective=NULL, interval=FALSE, cumulative=FALSE,
intervalType=c("n","p","l","sp","np","a"), level=0.95, ICs,
holdout=FALSE, insideinterval=NULL, errormeasures=NULL,
occurrence="n", obs=NULL, digits=5){
# Function forms the generic output for state space models.
if(!is.null(modelname)){
if(is.list(modelname)){
model <- "smoothC";
modelname <- "Combined smooth";
}
else{
if(gregexpr("ETS",modelname)!=-1){
model <- "ETS";
}
else if(gregexpr("CES",modelname)!=-1){
model <- "CES";
}
else if(gregexpr("GUM",modelname)!=-1){
model <- "GUM";
}
else if(gregexpr("ARIMA",modelname)!=-1){
model <- "ARIMA";
}
else if(gregexpr("SMA",modelname)!=-1){
model <- "SMA";
}
else if(gregexpr("CMA",modelname)!=-1){
model <- "CMA";
}
}
}
else{
model <- "smoothC";
modelname <- "Combined smooth";
}
cat(paste0("Time elapsed: ",round(as.numeric(timeelapsed,units="secs"),2)," seconds\n"));
cat(paste0("Model estimated: ",modelname,"\n"));
if(all(occurrence!=c("n","none","p","provided"))){
if(any(occurrence==c("f","fixed"))){
occurrence <- "Fixed probability";
}
else if(any(occurrence==c("o","odds-ratio"))){
occurrence <- "Odds ratio";
}
else if(any(occurrence==c("i","inverse-odds-ratio"))){
occurrence <- "Inverse odds ratio";
}
else if(any(occurrence==c("d","direct"))){
occurrence <- "Direct";
}
else if(any(occurrence==c("g","general"))){
occurrence <- "General";
}
cat(paste0("Occurrence model type: ",occurrence));
cat("\n");
}
else if(any(occurrence==c("p"))){
cat(paste0("Occurrence data provided for the holdout.\n"));
}
### Stuff for ETS
if(any(model==c("ETS","GUM"))){
if(!is.null(persistence)){
cat(paste0("Persistence vector g:\n"));
if(is.matrix(persistence)){
print(round(t(persistence),digits));
}
else{
print(round(persistence,digits));
}
}
if(!is.null(phi)){
if(gregexpr("d",modelname)!=-1){
cat(paste0("Damping parameter: ", round(phi,digits),"\n"));
}
}
}
### Stuff for GUM
if(model=="GUM"){
if(!is.null(transition)){
cat("Transition matrix F: \n");
print(round(transition,digits));
}
if(!is.null(measurement)){
cat(paste0("Measurement vector w: ",paste(round(measurement,digits),collapse=", "),"\n"));
}
}
### Stuff for ARIMA
if(model=="ARIMA"){
if(all(!is.null(ARterms))){
cat("Matrix of AR terms:\n");
print(round(ARterms,digits));
}
if(all(!is.null(MAterms))){
cat("Matrix of MA terms:\n");
print(round(MAterms,digits));
}
if(!is.null(constant)){
if(constant!=FALSE){
cat(paste0("Constant value is: ",round(constant,digits),"\n"));
}
}
}
### Stuff for CES
if(model=="CES"){
if(!is.null(a)){
cat(paste0("a0 + ia1: ",round(a,digits),"\n"));
}
if(!is.null(b)){
if(is.complex(b)){
cat(paste0("b0 + ib1: ",round(b,digits),"\n"));
}
else{
cat(paste0("b: ",round(b,digits),"\n"));
}
}
}
if(model!="CMA"){
if(initialType=="o"){
cat("Initial values were optimised.\n");
}
else if(initialType=="b"){
cat("Initial values were produced using backcasting.\n");
}
else if(initialType=="p"){
cat("Initial values were provided by user.\n");
}
}
if(hadxreg){
cat("Xreg coefficients were estimated");
if(wentwild){
cat(" in a crazy style\n");
}
else{
cat(" in a normal style\n");
}
}
cat(paste0("\nLoss function type: ",loss))
if(!is.null(cfObjective)){
cat(paste0("; Loss function value: ",round(cfObjective,digits)));
}
if(!is.null(s2)){
cat("\nError standard deviation: "); cat(round(sqrt(s2),digits));
cat("\n");
}
cat("Sample size: "); cat(obs);
cat("\n");
if(!is.null(nParam)){
cat("Number of estimated parameters: "); cat(nParam[1,4]);
cat("\n");
if(nParam[2,4]>0){
cat("Number of provided parameters: "); cat(nParam[2,4]);
cat("\n");
}
cat("Number of degrees of freedom: "); cat(obs-nParam[1,4]);
cat("\n");
}
cat("Information criteria:\n");
if(model=="ETS"){
if(any(unlist(gregexpr("C",modelname))!=-1)){
cat("(combined values)\n");
}
ICs <- ICs[nrow(ICs),];
}
print(round(ICs,digits));
if(interval){
cat("\n");
if(intervalType=="p"){
intervalType <- "parametric";
}
else if(intervalType=="l"){
intervalType <- "likelihood-based";
}
else if(intervalType=="sp"){
intervalType <- "semiparametric";
}
else if(intervalType=="np"){
intervalType <- "nonparametric";
}
else if(intervalType=="a"){
intervalType <- "asymmetric";
}
if(cumulative){
intervalType <- paste0("cumulative ",intervalType);
}
cat(paste0(level*100,"% ",intervalType," prediction interval was constructed"));
}
if(holdout){
cat("\n");
if(interval && !is.null(insideinterval)){
cat(paste0(round(insideinterval,0), "% of values are in the prediction interval\n"));
}
cat("Forecast errors:\n");
if(any(occurrence==c("none","n"))){
cat(paste(paste0("MPE: ",round(errormeasures["MPE"],3)*100,"%"),
paste0("sCE: ",round(errormeasures["sCE"],3)*100,"%"),
paste0("Asymmetry: ",round(errormeasures["asymmetry"],3)*100,"%"),
paste0("MAPE: ",round(errormeasures["MAPE"],3)*100,"%\n"),sep="; "));
cat(paste(paste0("MASE: ",round(errormeasures["MASE"],3)),
paste0("sMAE: ",round(errormeasures["sMAE"],3)*100,"%"),
paste0("sMSE: ",round(errormeasures["sMSE"],3)*100,"%"),
paste0("rMAE: ",round(errormeasures["rMAE"],3)),
paste0("rRMSE: ",round(errormeasures["rRMSE"],3),"\n"),sep="; "));
}
else{
cat(paste(paste0("Asymmetry: ",round(errormeasures["asymmetry"],3)*100,"%"),
paste0("sMSE: ",round(errormeasures["sMSE"],3)*100,"%"),
paste0("rRMSE: ",round(errormeasures["rRMSE"],3)),
paste0("sPIS: ",round(errormeasures["sPIS"],3)*100,"%"),
paste0("sCE: ",round(errormeasures["sCE"],3)*100,"%\n"),sep="; "));
}
}
}
debugger <- function(){
continue <- readline(prompt="Continue? ")
if(continue=="n"){
stop("The user asked to stop the calculations.");
}
}
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