fittestFunction <-
function(timeseries, timeseries.test=NULL, h=NULL, na.action=na.omit, level=0.9, se.fit=FALSE, ..., pars=NULL,
rank.by=c("MSE","NMSE","MAPE","sMAPE","MaxError","AIC","AICc","BIC","logLik","errors","fitness")){
#catch parameter errors
if(is.null(timeseries)) stop("timeseries is required and must have positive length")
if(is.null(timeseries.test) & is.null(h)) stop("the number of values to be predicted is unknown, provide either timeseries.test or h")
#prepare the training time series
#prepare the test time series (if present) and set the prediction horizon
n.ahead <- ts.test <- NULL
if(!is.null(timeseries.test)) {
ts.test <- ts(na.action(timeseries.test),start=i.n.ahead)
n.ahead <- length(ts.test)
if(!is.null(h)){
if(h < n.ahead){
ts.test <- head(ts.test,h)
n.ahead <- h
}
}
}
else n.ahead <- h
# evaluate choices of rank.by
rank.by <- match.arg(rank.by)
if(rank.by == "fitness") rank.by <- c("AIC","AICc","BIC","logLik")
else if(rank.by == "errors") rank.by <- c("MSE","NMSE","MAPE","sMAPE","MaxError")
#optimize Model given a set of initial parameters
optim.model <- function(timeseries, ...){
}
#computes quality measures acoording to rank.by
fitness.criteria <- function(model,npar,nobs){
#computes quality measures acoording to rank.by
ll <- logLik(model, marginal = TRUE)
AIC <- -2*ll+2*npar
BIC <- -2*ll+log(nobs)*npar
AICc <- AIC + 2*npar*(npar+1)/(nobs-npar-1)
return(data.frame(AICc=AICc,AIC=AIC,BIC=BIC,logLik=ll))
}
#computes predictions, and prediction error measures (if timeseries.test is provided)
pred.criteria <- function(model,n.ahead,level,se.fit,i.n.ahead,ts.test,ts){
#computes predictions using the candidate model
pred <- predict(model,n.ahead=n.ahead,interval="prediction",level=level, se.fit=se.fit)
pred.mean <- ts(pred[,1],start=i.n.ahead)
#computes prediction error measures if ts.test is provided
if(!is.null(ts.test)) {
MSE <- TSPred::MSE(ts.test, pred.mean)
NMSE <- TSPred::NMSE(ts.test, pred.mean, ts)
MAPE <- TSPred::MAPE(ts.test, pred.mean)
sMAPE <- TSPred::sMAPE(ts.test, pred.mean)
MaxError <- TSPred::MAXError(ts.test, pred.mean)
return(list(pred=pred,errors=data.frame(MSE=MSE,NMSE=NMSE,MAPE=MAPE,sMAPE=sMAPE,MaxError=MaxError)))
}
return(list(pred=pred))
}
#ranks candidate models
ranking.models <- function(rank,rank.by,models){
rownames(rank) <- NULL
#create ranking criteria based on all measures referenced by rank.by
criteria <- rank[ , (names(rank) %in% rank.by), drop = FALSE]
if("logLik" %in% names(criteria)) criteria["logLik"] <- -criteria["logLik"]
TSPredC <- 0
for(c in names(criteria)) TSPredC <- TSPredC + rank(criteria[c])
names(TSPredC) <- NULL
#ranking the candidate models based on all measures referenced by rank.by
rank <- cbind(rank,rank.position.sum=TSPredC)
rank <- rank[with(rank,order(rank.position.sum)),]
#candidate models are ranked and included as attribute of rank dataframe
models <- models[rank$ModelId]
attr(rank,"ranked.models") <- models
return(rank)
}
#if parameter is not provided, the function finds the best option among {...}
rank <- NULL
if(is.null(pars)){
# creates the Validation series for parameter optimization
ts.val <- tail(ts,n.ahead)
ts.tmp <- head(ts,nobs-n.ahead)
#if rank.by considers fitness measures, parameter optimization uses only and all the training series
if(any(c("AIC","AICc","BIC","logLik") %in% rank.by)) ts.tmp <- ts
#initial options of pars
pars.opt <- c(...)
#produces candidate models and measures in order to select "best" parameters
models <- list()
for(par in pars.opt){
#generates and optimizes candidate Model based on initial parameter values
model <- optim.model(ts.tmp, ...)
#creates candidate model id and saves it in the list models
ModelId <- paste(...)
models[[ModelId]] <- model
if(any(c("AIC","AICc","BIC","logLik") %in% rank.by)){
#computes fitness measures and returns a dataframe with them
rank.measures <- fitness.criteria(model,npar,length(ts.tmp))
}
else if(any(c("MSE","NMSE","MAPE","sMAPE","MaxError") %in% rank.by)){
#computes predictions and prediction error measures
rank.measures <- pred.criteria(model,n.ahead,level,filtered,TRUE,length(ts.tmp)+1,ts.val,ts.tmp)$errors
}
#combine results of the candidate models in the dataframe rank
rank <- rbind(rank, data.frame(ModelId=ModelId,par=par,rank.measures))
}
#ranking the candidate models based on all measures referenced by rank.by
#also candidate models (objects) are ranked and included as attribute of rank dataframe
rank <- ranking.models(rank,rank.by,models)
#if rank.by is fitness
par.optim <- rank[1,]$par
}
else{
par.optim <- pars
}
#generates and optimizes Model based on optim parameter values
if(any(c("AIC","AICc","BIC","logLik") %in% rank.by)){
model <- models[[1]]
}
else if(any(c("MSE","NMSE","MAPE","sMAPE","MaxError") %in% rank.by)){
model <- optim.model(ts, par.optim, ...)
}
#computes fitness measures and returns a dataframe with them
fit.measures <- fitness.criteria(model,npar,nobs)
fit.measures <- lapply(fit.measures,identity) #transforms to list
#computes predictions, and prediction error measures (if timeseries.test is provided)
pred.measures <- pred.criteria(model,n.ahead,level,se.fit,i.n.ahead,ts.test,ts)
#predictions
prediction <- pred.measures$pred
#error measures into list
errors.measures <- switch(is.null(pred.measures$errors)+1,lapply(pred.measures$errors,identity),NULL)
#append results in a list
results <- c( list(model=model), pars=par.optim, fit.measures, list(pred=prediction), errors.measures )
if(!is.null(rank) ) results <- c(results, list(rank.val=rank))
return(results)
}
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