R/fitBATS.R
In pli2016/forecast: Forecasting Functions for Time Series and Linear Models
# TODO:
#
# Author: srazbash
###############################################################################
fitPreviousBATSModel <- function (y, model, biasadj=FALSE) {
seasonal.periods <- model$seasonal.periods
if (is.null(seasonal.periods) == FALSE) {
seasonal.periods <- as.integer(sort(seasonal.periods))
}
paramz <- unParameterise(model$parameters$vect, model$parameters$control)
lambda <- paramz$lambda
alpha <- paramz$alpha
beta.v <- paramz$beta
small.phi <- paramz$small.phi
gamma.v <- paramz$gamma.v
ar.coefs <- paramz$ar.coefs
ma.coefs <- paramz$ma.coefs
p <- length(ar.coefs)
q <- length(ma.coefs)
##Calculate the variance:
#1. Re-set up the matrices
w <- .Call("makeBATSWMatrix", smallPhi_s = small.phi, sPeriods_s = seasonal.periods, arCoefs_s = ar.coefs, maCoefs_s = ma.coefs, PACKAGE = "forecast")
g <- .Call("makeBATSGMatrix", as.numeric(alpha), beta.v, gamma.v, seasonal.periods, as.integer(p), as.integer(q), PACKAGE="forecast")
F <- makeFMatrix(alpha=alpha, beta=beta.v, small.phi <- small.phi, seasonal.periods=seasonal.periods, gamma.bold.matrix=g$gamma.bold.matrix, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
#2. Calculate!
y.touse <- y
if (!is.null(lambda)) {
y.touse <- BoxCox(y, lambda=lambda)
}
fitted.values.and.errors <- calcModel(y.touse, model$seed.states, F, g$g, w)
e <- fitted.values.and.errors$e
fitted.values <- fitted.values.and.errors$y.hat
variance <- sum((e*e))/length(y)
if (!is.null(lambda)) {
fitted.values <- InvBoxCox(fitted.values, lambda=lambda, biasadj, variance)
}
model.for.output <- model
model.for.output$variance = variance
model.for.output$fitted.values = c(fitted.values)
model.for.output$errors=c(e)
model.for.output$x=fitted.values.and.errors$x
model.for.output$y=y
return(model.for.output)
}
fitSpecificBATS <- function(y, use.box.cox, use.beta, use.damping, seasonal.periods=NULL, starting.params=NULL, x.nought=NULL, ar.coefs=NULL, ma.coefs=NULL, init.box.cox=NULL, bc.lower=0, bc.upper=1, biasadj=FALSE) {
if(!is.null(seasonal.periods)) {
seasonal.periods <- as.integer(sort(seasonal.periods))
}
##Meaning/purpose of the first if() statement: If this is the first pass, then use default starting values. Else if it is the second pass, then use the values form the first pass as starting values.
if(is.null(starting.params)) {
##Check for the existence of ARMA() coefficients
if(!is.null(ar.coefs)) {
p <- length(ar.coefs)
} else {
p <- 0
}
if(!is.null(ma.coefs)) {
q <- length(ma.coefs)
} else {
q <- 0
}
#Calculate starting values:
if(sum(seasonal.periods) > 16) {
alpha <- (1e-6)
} else {
alpha <- .1
}
if(use.beta) {
if(sum(seasonal.periods) > 16) {
beta.v <- (5e-7)
} else {
beta.v <- .05
}
b <- 0.00
if(use.damping) {
small.phi <- .999
} else {
small.phi <- 1
}
} else {
beta.v <- NULL
b <- NULL
small.phi <- NULL
use.damping=FALSE
}
if(!is.null(seasonal.periods)) {
gamma.v <- rep(.001, length(seasonal.periods))
s.vector <- numeric(sum(seasonal.periods))
#for(s in seasonal.periods) {
# s.vector <- cbind(s.vector, numeric(s))
#}
} else {
gamma.v <- NULL
s.vector <- NULL
}
if(use.box.cox) {
if(!is.null(init.box.cox)) {
lambda<-init.box.cox
} else {
lambda <- BoxCox.lambda(y, lower=0, upper=1.5)
}
y.transformed <- BoxCox(y, lambda=lambda)
} else { #the "else" is not needed at the moment
lambda <- NULL
}
} else {
paramz <- unParameterise(starting.params$vect, starting.params$control)
lambda <- paramz$lambda
alpha <- paramz$alpha
beta.v <- paramz$beta
b <- 0
small.phi <- paramz$small.phi
gamma.v <- paramz$gamma.v
if(!is.null(seasonal.periods)) {
s.vector <- numeric(sum(seasonal.periods))
} else {
s.vector <- NULL
}
#ar.coefs <- paramz$ar.coefs
#ma.coefs <- paramz$ma.coefs
##Check for the existence of ARMA() coefficients
if(!is.null(ar.coefs)) {
p <- length(ar.coefs)
} else {
p <- 0
}
if(!is.null(ma.coefs)) {
q <- length(ma.coefs)
} else {
q <- 0
}
}
if(is.null(x.nought)) {
#Start with the seed states equal to zero
if(!is.null(ar.coefs)) {
d.vector <- numeric(length(ar.coefs))
} else {
d.vector <- NULL
}
if(!is.null(ma.coefs)) {
epsilon.vector <- numeric(length(ma.coefs))
} else {
epsilon.vector <- NULL
}
x.nought <- makeXMatrix(l=0,b=b, s.vector=s.vector, d.vector=d.vector, epsilon.vector=epsilon.vector)$x
}
##Optimise the starting values:
#Make the parameter vector parameterise
param.vector <- parameterise(alpha=alpha, beta.v=beta.v, small.phi=small.phi, gamma.v=gamma.v, lambda=lambda, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
par.scale <- makeParscaleBATS(param.vector$control)
#w <- makeWMatrix(small.phi=small.phi, seasonal.periods=seasonal.periods, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
w <- .Call("makeBATSWMatrix", smallPhi_s = small.phi, sPeriods_s = seasonal.periods, arCoefs_s = ar.coefs, maCoefs_s = ma.coefs, PACKAGE = "forecast")
#g <- makeGMatrix(alpha=alpha, beta=beta.v, gamma.vector=gamma, seasonal.periods=seasonal.periods, p=p, q=q)
g <- .Call("makeBATSGMatrix", as.numeric(alpha), beta.v, gamma.v, seasonal.periods, as.integer(p), as.integer(q), PACKAGE="forecast")
F <- makeFMatrix(alpha=alpha, beta=beta.v, small.phi=small.phi, seasonal.periods=seasonal.periods, gamma.bold.matrix=g$gamma.bold.matrix, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
D <- F - g$g %*% w$w.transpose
##Set up matrices to find the seed states
if(use.box.cox) {
y.transformed <- BoxCox(y, lambda=lambda)
#x.nought <- BoxCox(x.nought, lambda=lambda)
y.tilda <- calcModel(y.transformed, x.nought, F, g$g, w)$e
} else {
y.tilda <- calcModel(y, x.nought, F, g$g, w)$e
}
w.tilda.transpose <- matrix(0, nrow=length(y), ncol=ncol(w$w.transpose))
w.tilda.transpose[1,] <- w$w.transpose
#for(i in 2:length(y)) {
# w.tilda.transpose[i,] <- w.tilda.transpose[(i-1),] %*% D
#}
w.tilda.transpose=.Call("calcWTilda", wTildaTransposes=w.tilda.transpose, Ds=D, PACKAGE = "forecast")
##If there is a seasonal component in the model, then the follow adjustment need to be made so that the seed states can be found
if(!is.null(seasonal.periods)) {
#drop the lines from w.tilda.transpose that correspond to the last seasonal value of each seasonal period
list.cut.w <- cutW(use.beta=use.beta, w.tilda.transpose=w.tilda.transpose, seasonal.periods=seasonal.periods, p=p, q=q)
w.tilda.transpose <- list.cut.w$matrix
mask.vector <- list.cut.w$mask.vector
##Run the regression to find the SEED STATES
coefs <- lm(t(y.tilda) ~ w.tilda.transpose - 1)$coefficients
##Find the ACTUAL SEASONAL seed states
x.nought <- calcSeasonalSeeds(use.beta=use.beta, coefs=coefs, seasonal.periods=seasonal.periods, mask.vector=mask.vector, p=p, q=q)
} else {
#Remove the AR() and MA() bits if they exist
if((p != 0) | (q != 0)) {
end.cut <- ncol(w.tilda.transpose)
start.cut <- end.cut-(p+q)+1
w.tilda.transpose <- w.tilda.transpose[,-c(start.cut:end.cut)]
}
x.nought <- lm(t(y.tilda) ~ w.tilda.transpose - 1)$coefficients
x.nought <- matrix(x.nought, nrow=length(x.nought), ncol=1)
##Replace the AR() and MA() bits if they exist
if((p != 0) | (q != 0)) {
arma.seed.states <- numeric((p+q))
arma.seed.states <- matrix(arma.seed.states, nrow=length(arma.seed.states), ncol=1)
x.nought <- rbind(x.nought, arma.seed.states)
}
}
####
#Set up environment
opt.env <- new.env()
assign("F", F, envir=opt.env)
assign("w.transpose", w$w.transpose, envir=opt.env)
assign("g", g$g, envir=opt.env)
assign("gamma.bold.matrix", g$gamma.bold.matrix, envir=opt.env)
assign("y", matrix(y, nrow=1, ncol=length(y)), envir=opt.env)
assign("y.hat", matrix(0, nrow=1, ncol=length(y)), envir=opt.env)
assign("e", matrix(0, nrow=1, ncol=length(y)), envir=opt.env)
assign("x", matrix(0, nrow=length(x.nought), ncol=length(y)), envir=opt.env)
if(!is.null(seasonal.periods)) {
tau <- sum(seasonal.periods)
} else {
tau <- 0
}
##Second pass of optimisation
if(use.box.cox) {
#Un-transform the seed states
#x.nought.untransformed <- InvBoxCox(x.nought, lambda=lambda)
assign("x.nought.untransformed", InvBoxCox(x.nought, lambda=lambda), envir=opt.env)
#Optimise the likelihood function
optim.like <- optim(par=param.vector$vect, fn=calcLikelihood, method="Nelder-Mead", opt.env=opt.env, use.beta=use.beta, use.small.phi=use.damping, seasonal.periods=seasonal.periods, p=p, q=q, tau=tau, bc.lower=bc.lower, bc.upper=bc.upper, control=list(maxit=(100*length(param.vector$vect)^2), parscale=par.scale))
#Get the parameters out of the param.vector
paramz <- unParameterise(optim.like$par, param.vector$control)
lambda <- paramz$lambda
alpha <- paramz$alpha
beta.v <- paramz$beta
small.phi <- paramz$small.phi
gamma.v <- paramz$gamma.v
ar.coefs <- paramz$ar.coefs
ma.coefs <- paramz$ma.coefs
#Transform the seed states
x.nought <- BoxCox(opt.env$x.nought.untransformed, lambda=lambda)
##Calculate the variance:
#1. Re-set up the matrices
#w <- makeWMatrix(small.phi=small.phi, seasonal.periods=seasonal.periods, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
w <- .Call("makeBATSWMatrix", smallPhi_s = small.phi, sPeriods_s = seasonal.periods, arCoefs_s = ar.coefs, maCoefs_s = ma.coefs, PACKAGE = "forecast")
#g <- makeGMatrix(alpha=alpha, beta=beta.v, gamma.vector=gamma, seasonal.periods=seasonal.periods, p=p, q=q)
g <- .Call("makeBATSGMatrix", as.numeric(alpha), beta.v, gamma.v, seasonal.periods, as.integer(p), as.integer(q), PACKAGE="forecast")
F <- makeFMatrix(alpha=alpha, beta=beta.v, small.phi=small.phi, seasonal.periods=seasonal.periods, gamma.bold.matrix=g$gamma.bold.matrix, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
#2. Calculate!
y.transformed <- BoxCox(y, lambda=lambda)
fitted.values.and.errors <- calcModel(y.transformed, x.nought, F, g$g, w)
e <- fitted.values.and.errors$e
variance <- sum((e*e))/length(y)
fitted.values <- InvBoxCox(fitted.values.and.errors$y.hat, lambda=lambda, biasadj, variance)
attr(lambda, "biasadj") <- biasadj
#e <- InvBoxCox(e, lambda=lambda)
#ee <- y-fitted.values
} else { #else if we are not using the Box-Cox transformation
#Optimise the likelihood function
if(length(param.vector$vect) > 1) {
optim.like <- optim(par=param.vector$vect, fn=calcLikelihoodNOTransformed, method="Nelder-Mead", opt.env=opt.env, x.nought=x.nought, use.beta=use.beta, use.small.phi=use.damping, seasonal.periods=seasonal.periods, p=p, q=q, tau=tau, control=list(maxit=(100*length(param.vector$vect)^2), parscale=par.scale))
} else {
optim.like <- optim(par=param.vector$vect, fn=calcLikelihoodNOTransformed, method="BFGS", opt.env=opt.env, x.nought=x.nought, use.beta=use.beta, use.small.phi=use.damping, seasonal.periods=seasonal.periods, p=p, q=q, tau=tau, control=list(parscale=par.scale))
}
#Get the parameters out of the param.vector
paramz <- unParameterise(optim.like$par, param.vector$control)
lambda <- paramz$lambda
alpha <- paramz$alpha
beta.v <- paramz$beta
small.phi <- paramz$small.phi
gamma.v <- paramz$gamma.v
ar.coefs <- paramz$ar.coefs
ma.coefs <- paramz$ma.coefs
##Calculate the variance:
#1. Re-set up the matrices
#w <- makeWMatrix(small.phi=small.phi, seasonal.periods=seasonal.periods, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
w <- .Call("makeBATSWMatrix", smallPhi_s = small.phi, sPeriods_s = seasonal.periods, arCoefs_s = ar.coefs, maCoefs_s = ma.coefs, PACKAGE = "forecast")
#g <- makeGMatrix(alpha=alpha, beta=beta.v, gamma.vector=gamma, seasonal.periods=seasonal.periods, p=p, q=q)
g <- .Call("makeBATSGMatrix", as.numeric(alpha), beta.v, gamma.v, seasonal.periods, as.integer(p), as.integer(q), PACKAGE="forecast")
F <- makeFMatrix(alpha=alpha, beta=beta.v, small.phi <- small.phi, seasonal.periods=seasonal.periods, gamma.bold.matrix=g$gamma.bold.matrix, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
#2. Calculate!
fitted.values.and.errors <- calcModel(y, x.nought, F, g$g, w)
e <- fitted.values.and.errors$e
fitted.values <- fitted.values.and.errors$y.hat
variance <- sum((e*e))/length(y)
}
#Get the likelihood
likelihood <- optim.like$value
#Calculate the AIC
aic <- likelihood+2*(length(param.vector$vect)+nrow(x.nought))
#Make a list object
model.for.output <- list(lambda=lambda, alpha=alpha, beta=beta.v, damping.parameter=small.phi, gamma.values=gamma.v, ar.coefficients=ar.coefs, ma.coefficients=ma.coefs, likelihood=likelihood, optim.return.code=optim.like$convergence, variance=variance, AIC=aic, parameters=list(vect=optim.like$par, control=param.vector$control), seed.states=x.nought, fitted.values=c(fitted.values), errors=c(e), x=fitted.values.and.errors$x, seasonal.periods=seasonal.periods, y=y)
class(model.for.output) <- "bats"
####
return(model.for.output)
}
calcModel <- function(y, x.nought, F, g, w) { #w is passed as a list
length.ts <- length(y)
x <- matrix(0, nrow=length(x.nought), ncol=length.ts)
y.hat <- matrix(0,nrow=1, ncol=length.ts)
e <- matrix(0, nrow=1, ncol=length.ts)
y.hat[,1] <- w$w.transpose %*% x.nought
e[,1] <- y[1]-y.hat[,1]
x[,1] <- F %*% x.nought + g %*% e[,1]
y <- matrix(y, nrow=1, ncol=length.ts)
loop <- .Call( "calcBATS", ys=y, yHats=y.hat, wTransposes = w$w.transpose, Fs=F, xs=x, gs=g, es=e, PACKAGE = "forecast" )
return(list(y.hat=loop$y.hat, e=loop$e, x=loop$x))
}
calcLikelihood <- function(param.vector, opt.env, use.beta, use.small.phi, seasonal.periods, p=0, q=0, tau=0, bc.lower=0, bc.upper=1) {
#param vector should be as follows: Box-Cox.parameter, alpha, beta, small.phi, gamma.vector, ar.coefs, ma.coefs
#Put the components of the param.vector into meaningful individual variables
box.cox.parameter <- param.vector[1]
alpha <- param.vector[2]
if(use.beta) {
if(use.small.phi) {
small.phi <- param.vector[3]
beta.v <- param.vector[4]
gamma.start <- 5
} else {
small.phi <- 1
beta.v <- param.vector[3]
gamma.start <- 4
}
} else {
small.phi <- NULL
beta.v <- NULL
gamma.start <- 3
}
if(!is.null(seasonal.periods)) {
gamma.vector <- param.vector[gamma.start:(gamma.start+length(seasonal.periods)-1)]
final.gamma.pos <- gamma.start+length(gamma.vector)-1
} else {
gamma.vector=NULL
final.gamma.pos <- gamma.start-1
}
if(p != 0) {
ar.coefs <- matrix(param.vector[(final.gamma.pos+1):(final.gamma.pos+p)], nrow=1, ncol=p)
} else {
ar.coefs <- NULL
}
if(q != 0) {
ma.coefs <- matrix(param.vector[(final.gamma.pos+p+1):length(param.vector)], nrow=1, ncol=q)
} else {
ma.coefs <- NULL
}
x.nought <- BoxCox(opt.env$x.nought.untransformed, lambda=box.cox.parameter)
#w <- makeWMatrix(small.phi=small.phi, seasonal.periods=seasonal.periods, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
#w <- .Call("makeBATSWMatrix", smallPhi_s = small.phi, sPeriods_s = seasonal.periods, arCoefs_s = ar.coefs, maCoefs_s = ma.coefs, PACKAGE = "forecast")
.Call("updateWtransposeMatrix", wTranspose_s=opt.env$w.transpose, smallPhi_s=small.phi, tau_s=as.integer(tau), arCoefs_s=ar.coefs, maCoefs_s=ma.coefs, p_s=as.integer(p), q_s=as.integer(q), PACKAGE = "forecast")
#g <- makeGMatrix(alpha=alpha, beta=beta, gamma.vector=gamma.vector, seasonal.periods=seasonal.periods, p=p, q=q)
#g <- .Call("makeBATSGMatrix", as.numeric(alpha), beta.v, gamma.vector, seasonal.periods, as.integer(p), as.integer(q), PACKAGE="forecast")
.Call("updateGMatrix", g_s=opt.env$g, gammaBold_s=opt.env$gamma.bold.matrix, alpha_s=alpha, beta_s=beta.v, gammaVector_s=gamma.vector, seasonalPeriods_s=seasonal.periods, PACKAGE="forecast")
#F <- makeFMatrix(alpha=alpha, beta=beta.v, small.phi=small.phi, seasonal.periods=seasonal.periods, gamma.bold.matrix=g$gamma.bold.matrix, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
.Call("updateFMatrix", opt.env$F, small.phi, alpha, beta.v, opt.env$gamma.bold.matrix, ar.coefs, ma.coefs, tau, PACKAGE="forecast")
mat.transformed.y <- BoxCox(opt.env$y, box.cox.parameter)
n <- ncol(opt.env$y)
.Call( "calcBATSFaster", ys=mat.transformed.y, yHats=opt.env$y.hat, wTransposes = opt.env$w.transpose, Fs=opt.env$F, xs=opt.env$x, gs=opt.env$g, es=opt.env$e, xNought_s = x.nought, sPeriods_s = seasonal.periods, betaV = beta.v, tau_s = as.integer(tau), p_s = as.integer(p), q_s = as.integer(q), PACKAGE = "forecast" )
log.likelihood <- n*log(sum(opt.env$e^2))-2*(box.cox.parameter-1)*sum(log(opt.env$y))
assign("D", (opt.env$F - opt.env$g %*% opt.env$w.transpose), envir=opt.env)
if(checkAdmissibility(opt.env, box.cox=box.cox.parameter, small.phi=small.phi, ar.coefs=ar.coefs, ma.coefs=ma.coefs, tau=tau, bc.lower=bc.lower, bc.upper=bc.upper)) {
return(log.likelihood)
} else {
return(10^20)
}
}
calcLikelihoodNOTransformed <- function(param.vector, opt.env, x.nought, use.beta, use.small.phi, seasonal.periods, p=0, q=0, tau=0) {
#The likelihood function without the Box-Cox Transformation
#param vector should be as follows: alpha, beta, small.phi, gamma.vector, ar.coefs, ma.coefs
#Put the components of the param.vector into meaningful individual variables
alpha <- param.vector[1]
if(use.beta) {
if(use.small.phi) {
small.phi <- param.vector[2]
beta.v <- param.vector[3]
gamma.start <- 4
} else {
small.phi <- 1
beta.v <- param.vector[2]
gamma.start <- 3
}
} else {
small.phi <- NULL
beta.v <- NULL
gamma.start <- 2
}
if(!is.null(seasonal.periods)) {
gamma.vector <- param.vector[gamma.start:(gamma.start+length(seasonal.periods)-1)]
final.gamma.pos <- gamma.start+length(gamma.vector)-1
} else {
gamma.vector=NULL
final.gamma.pos <- gamma.start-1
}
if(p != 0) {
ar.coefs <- matrix(param.vector[(final.gamma.pos+1):(final.gamma.pos+p)], nrow=1, ncol=p)
} else {
ar.coefs <- NULL
}
if(q != 0) {
ma.coefs <- matrix(param.vector[(final.gamma.pos+p+1):length(param.vector)], nrow=1, ncol=q)
} else {
ma.coefs <- NULL
}
#w <- makeWMatrix(small.phi=small.phi, seasonal.periods=seasonal.periods, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
#w <- .Call("makeBATSWMatrix", smallPhi_s = small.phi, sPeriods_s = seasonal.periods, arCoefs_s = ar.coefs, maCoefs_s = ma.coefs, PACKAGE = "forecast")
.Call("updateWtransposeMatrix", wTranspose_s=opt.env$w.transpose, smallPhi_s=small.phi, tau_s=as.integer(tau), arCoefs_s=ar.coefs, maCoefs_s=ma.coefs, p_s=as.integer(p), q_s=as.integer(q), PACKAGE = "forecast")
#g <- makeGMatrix(alpha=alpha, beta=beta, gamma.vector=gamma.vector, seasonal.periods=seasonal.periods, p=p, q=q)
#g <- .Call("makeBATSGMatrix", alpha, beta.v, gamma.vector, seasonal.periods, as.integer(p), as.integer(q), PACKAGE="forecast")
.Call("updateGMatrix", g_s=opt.env$g, gammaBold_s=opt.env$gamma.bold.matrix, alpha_s=alpha, beta_s=beta.v, gammaVector_s=gamma.vector, seasonalPeriods_s=seasonal.periods, PACKAGE="forecast")
#F <- makeFMatrix(alpha=alpha, beta=beta.v, small.phi=small.phi, seasonal.periods=seasonal.periods, gamma.bold.matrix=g$gamma.bold.matrix, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
.Call("updateFMatrix", opt.env$F, small.phi, alpha, beta.v, opt.env$gamma.bold.matrix, ar.coefs, ma.coefs, tau, PACKAGE="forecast")
n <- ncol(opt.env$y)
#########################################################################################
#e <- calcModel(y=y, x.nought=x.nought, F=F, g=g$g, w=w)$e
######################
#### calcModel() code:
##
#x <- matrix(0, nrow=length(x.nought), ncol=n)
#y.hat <- matrix(0,nrow=1, ncol=n)
#e <- matrix(0, nrow=1, ncol=n)
#opt.env$y.hat[,1] <- w$w.transpose %*% x.nought
#opt.env$e[,1] <- opt.env$y[,1]-opt.env$y.hat[,1]
#opt.env$x[,1] <- opt.env$F %*% x.nought + g$g %*% opt.env$e[,1]
#mat.y <- matrix(opt.env$y, nrow=1, ncol=n)
.Call( "calcBATSFaster", ys=opt.env$y, yHats=opt.env$y.hat, wTransposes = opt.env$w.transpose, Fs=opt.env$F, xs=opt.env$x, gs=opt.env$g, es=opt.env$e, xNought_s = x.nought, sPeriods_s = seasonal.periods, betaV = beta.v, tau_s = as.integer(tau), p_s = as.integer(p), q_s = as.integer(q) , PACKAGE = "forecast" )
##
####
####################################################################
log.likelihood <- n*log(sum(opt.env$e*opt.env$e))
#D <- opt.env$F - g$g %*% w$w.transpose
assign("D", (opt.env$F - opt.env$g %*% opt.env$w.transpose), envir=opt.env)
if(checkAdmissibility(opt.env=opt.env, box.cox=NULL, small.phi=small.phi, ar.coefs=ar.coefs, ma.coefs=ma.coefs, tau=tau)) {
return(log.likelihood)
} else {
return(10^20)
}
}
pli2016/forecast documentation built on May 25, 2019, 8:22 a.m.
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# TODO:
#
# Author: srazbash
###############################################################################
fitPreviousBATSModel <- function (y, model, biasadj=FALSE) {
seasonal.periods <- model$seasonal.periods
if (is.null(seasonal.periods) == FALSE) {
seasonal.periods <- as.integer(sort(seasonal.periods))
}
paramz <- unParameterise(model$parameters$vect, model$parameters$control)
lambda <- paramz$lambda
alpha <- paramz$alpha
beta.v <- paramz$beta
small.phi <- paramz$small.phi
gamma.v <- paramz$gamma.v
ar.coefs <- paramz$ar.coefs
ma.coefs <- paramz$ma.coefs
p <- length(ar.coefs)
q <- length(ma.coefs)
##Calculate the variance:
#1. Re-set up the matrices
w <- .Call("makeBATSWMatrix", smallPhi_s = small.phi, sPeriods_s = seasonal.periods, arCoefs_s = ar.coefs, maCoefs_s = ma.coefs, PACKAGE = "forecast")
g <- .Call("makeBATSGMatrix", as.numeric(alpha), beta.v, gamma.v, seasonal.periods, as.integer(p), as.integer(q), PACKAGE="forecast")
F <- makeFMatrix(alpha=alpha, beta=beta.v, small.phi <- small.phi, seasonal.periods=seasonal.periods, gamma.bold.matrix=g$gamma.bold.matrix, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
#2. Calculate!
y.touse <- y
if (!is.null(lambda)) {
y.touse <- BoxCox(y, lambda=lambda)
}
fitted.values.and.errors <- calcModel(y.touse, model$seed.states, F, g$g, w)
e <- fitted.values.and.errors$e
fitted.values <- fitted.values.and.errors$y.hat
variance <- sum((e*e))/length(y)
if (!is.null(lambda)) {
fitted.values <- InvBoxCox(fitted.values, lambda=lambda, biasadj, variance)
}
model.for.output <- model
model.for.output$variance = variance
model.for.output$fitted.values = c(fitted.values)
model.for.output$errors=c(e)
model.for.output$x=fitted.values.and.errors$x
model.for.output$y=y
return(model.for.output)
}
fitSpecificBATS <- function(y, use.box.cox, use.beta, use.damping, seasonal.periods=NULL, starting.params=NULL, x.nought=NULL, ar.coefs=NULL, ma.coefs=NULL, init.box.cox=NULL, bc.lower=0, bc.upper=1, biasadj=FALSE) {
if(!is.null(seasonal.periods)) {
seasonal.periods <- as.integer(sort(seasonal.periods))
}
##Meaning/purpose of the first if() statement: If this is the first pass, then use default starting values. Else if it is the second pass, then use the values form the first pass as starting values.
if(is.null(starting.params)) {
##Check for the existence of ARMA() coefficients
if(!is.null(ar.coefs)) {
p <- length(ar.coefs)
} else {
p <- 0
}
if(!is.null(ma.coefs)) {
q <- length(ma.coefs)
} else {
q <- 0
}
#Calculate starting values:
if(sum(seasonal.periods) > 16) {
alpha <- (1e-6)
} else {
alpha <- .1
}
if(use.beta) {
if(sum(seasonal.periods) > 16) {
beta.v <- (5e-7)
} else {
beta.v <- .05
}
b <- 0.00
if(use.damping) {
small.phi <- .999
} else {
small.phi <- 1
}
} else {
beta.v <- NULL
b <- NULL
small.phi <- NULL
use.damping=FALSE
}
if(!is.null(seasonal.periods)) {
gamma.v <- rep(.001, length(seasonal.periods))
s.vector <- numeric(sum(seasonal.periods))
#for(s in seasonal.periods) {
# s.vector <- cbind(s.vector, numeric(s))
#}
} else {
gamma.v <- NULL
s.vector <- NULL
}
if(use.box.cox) {
if(!is.null(init.box.cox)) {
lambda<-init.box.cox
} else {
lambda <- BoxCox.lambda(y, lower=0, upper=1.5)
}
y.transformed <- BoxCox(y, lambda=lambda)
} else { #the "else" is not needed at the moment
lambda <- NULL
}
} else {
paramz <- unParameterise(starting.params$vect, starting.params$control)
lambda <- paramz$lambda
alpha <- paramz$alpha
beta.v <- paramz$beta
b <- 0
small.phi <- paramz$small.phi
gamma.v <- paramz$gamma.v
if(!is.null(seasonal.periods)) {
s.vector <- numeric(sum(seasonal.periods))
} else {
s.vector <- NULL
}
#ar.coefs <- paramz$ar.coefs
#ma.coefs <- paramz$ma.coefs
##Check for the existence of ARMA() coefficients
if(!is.null(ar.coefs)) {
p <- length(ar.coefs)
} else {
p <- 0
}
if(!is.null(ma.coefs)) {
q <- length(ma.coefs)
} else {
q <- 0
}
}
if(is.null(x.nought)) {
#Start with the seed states equal to zero
if(!is.null(ar.coefs)) {
d.vector <- numeric(length(ar.coefs))
} else {
d.vector <- NULL
}
if(!is.null(ma.coefs)) {
epsilon.vector <- numeric(length(ma.coefs))
} else {
epsilon.vector <- NULL
}
x.nought <- makeXMatrix(l=0,b=b, s.vector=s.vector, d.vector=d.vector, epsilon.vector=epsilon.vector)$x
}
##Optimise the starting values:
#Make the parameter vector parameterise
param.vector <- parameterise(alpha=alpha, beta.v=beta.v, small.phi=small.phi, gamma.v=gamma.v, lambda=lambda, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
par.scale <- makeParscaleBATS(param.vector$control)
#w <- makeWMatrix(small.phi=small.phi, seasonal.periods=seasonal.periods, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
w <- .Call("makeBATSWMatrix", smallPhi_s = small.phi, sPeriods_s = seasonal.periods, arCoefs_s = ar.coefs, maCoefs_s = ma.coefs, PACKAGE = "forecast")
#g <- makeGMatrix(alpha=alpha, beta=beta.v, gamma.vector=gamma, seasonal.periods=seasonal.periods, p=p, q=q)
g <- .Call("makeBATSGMatrix", as.numeric(alpha), beta.v, gamma.v, seasonal.periods, as.integer(p), as.integer(q), PACKAGE="forecast")
F <- makeFMatrix(alpha=alpha, beta=beta.v, small.phi=small.phi, seasonal.periods=seasonal.periods, gamma.bold.matrix=g$gamma.bold.matrix, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
D <- F - g$g %*% w$w.transpose
##Set up matrices to find the seed states
if(use.box.cox) {
y.transformed <- BoxCox(y, lambda=lambda)
#x.nought <- BoxCox(x.nought, lambda=lambda)
y.tilda <- calcModel(y.transformed, x.nought, F, g$g, w)$e
} else {
y.tilda <- calcModel(y, x.nought, F, g$g, w)$e
}
w.tilda.transpose <- matrix(0, nrow=length(y), ncol=ncol(w$w.transpose))
w.tilda.transpose[1,] <- w$w.transpose
#for(i in 2:length(y)) {
# w.tilda.transpose[i,] <- w.tilda.transpose[(i-1),] %*% D
#}
w.tilda.transpose=.Call("calcWTilda", wTildaTransposes=w.tilda.transpose, Ds=D, PACKAGE = "forecast")
##If there is a seasonal component in the model, then the follow adjustment need to be made so that the seed states can be found
if(!is.null(seasonal.periods)) {
#drop the lines from w.tilda.transpose that correspond to the last seasonal value of each seasonal period
list.cut.w <- cutW(use.beta=use.beta, w.tilda.transpose=w.tilda.transpose, seasonal.periods=seasonal.periods, p=p, q=q)
w.tilda.transpose <- list.cut.w$matrix
mask.vector <- list.cut.w$mask.vector
##Run the regression to find the SEED STATES
coefs <- lm(t(y.tilda) ~ w.tilda.transpose - 1)$coefficients
##Find the ACTUAL SEASONAL seed states
x.nought <- calcSeasonalSeeds(use.beta=use.beta, coefs=coefs, seasonal.periods=seasonal.periods, mask.vector=mask.vector, p=p, q=q)
} else {
#Remove the AR() and MA() bits if they exist
if((p != 0) | (q != 0)) {
end.cut <- ncol(w.tilda.transpose)
start.cut <- end.cut-(p+q)+1
w.tilda.transpose <- w.tilda.transpose[,-c(start.cut:end.cut)]
}
x.nought <- lm(t(y.tilda) ~ w.tilda.transpose - 1)$coefficients
x.nought <- matrix(x.nought, nrow=length(x.nought), ncol=1)
##Replace the AR() and MA() bits if they exist
if((p != 0) | (q != 0)) {
arma.seed.states <- numeric((p+q))
arma.seed.states <- matrix(arma.seed.states, nrow=length(arma.seed.states), ncol=1)
x.nought <- rbind(x.nought, arma.seed.states)
}
}
####
#Set up environment
opt.env <- new.env()
assign("F", F, envir=opt.env)
assign("w.transpose", w$w.transpose, envir=opt.env)
assign("g", g$g, envir=opt.env)
assign("gamma.bold.matrix", g$gamma.bold.matrix, envir=opt.env)
assign("y", matrix(y, nrow=1, ncol=length(y)), envir=opt.env)
assign("y.hat", matrix(0, nrow=1, ncol=length(y)), envir=opt.env)
assign("e", matrix(0, nrow=1, ncol=length(y)), envir=opt.env)
assign("x", matrix(0, nrow=length(x.nought), ncol=length(y)), envir=opt.env)
if(!is.null(seasonal.periods)) {
tau <- sum(seasonal.periods)
} else {
tau <- 0
}
##Second pass of optimisation
if(use.box.cox) {
#Un-transform the seed states
#x.nought.untransformed <- InvBoxCox(x.nought, lambda=lambda)
assign("x.nought.untransformed", InvBoxCox(x.nought, lambda=lambda), envir=opt.env)
#Optimise the likelihood function
optim.like <- optim(par=param.vector$vect, fn=calcLikelihood, method="Nelder-Mead", opt.env=opt.env, use.beta=use.beta, use.small.phi=use.damping, seasonal.periods=seasonal.periods, p=p, q=q, tau=tau, bc.lower=bc.lower, bc.upper=bc.upper, control=list(maxit=(100*length(param.vector$vect)^2), parscale=par.scale))
#Get the parameters out of the param.vector
paramz <- unParameterise(optim.like$par, param.vector$control)
lambda <- paramz$lambda
alpha <- paramz$alpha
beta.v <- paramz$beta
small.phi <- paramz$small.phi
gamma.v <- paramz$gamma.v
ar.coefs <- paramz$ar.coefs
ma.coefs <- paramz$ma.coefs
#Transform the seed states
x.nought <- BoxCox(opt.env$x.nought.untransformed, lambda=lambda)
##Calculate the variance:
#1. Re-set up the matrices
#w <- makeWMatrix(small.phi=small.phi, seasonal.periods=seasonal.periods, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
w <- .Call("makeBATSWMatrix", smallPhi_s = small.phi, sPeriods_s = seasonal.periods, arCoefs_s = ar.coefs, maCoefs_s = ma.coefs, PACKAGE = "forecast")
#g <- makeGMatrix(alpha=alpha, beta=beta.v, gamma.vector=gamma, seasonal.periods=seasonal.periods, p=p, q=q)
g <- .Call("makeBATSGMatrix", as.numeric(alpha), beta.v, gamma.v, seasonal.periods, as.integer(p), as.integer(q), PACKAGE="forecast")
F <- makeFMatrix(alpha=alpha, beta=beta.v, small.phi=small.phi, seasonal.periods=seasonal.periods, gamma.bold.matrix=g$gamma.bold.matrix, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
#2. Calculate!
y.transformed <- BoxCox(y, lambda=lambda)
fitted.values.and.errors <- calcModel(y.transformed, x.nought, F, g$g, w)
e <- fitted.values.and.errors$e
variance <- sum((e*e))/length(y)
fitted.values <- InvBoxCox(fitted.values.and.errors$y.hat, lambda=lambda, biasadj, variance)
attr(lambda, "biasadj") <- biasadj
#e <- InvBoxCox(e, lambda=lambda)
#ee <- y-fitted.values
} else { #else if we are not using the Box-Cox transformation
#Optimise the likelihood function
if(length(param.vector$vect) > 1) {
optim.like <- optim(par=param.vector$vect, fn=calcLikelihoodNOTransformed, method="Nelder-Mead", opt.env=opt.env, x.nought=x.nought, use.beta=use.beta, use.small.phi=use.damping, seasonal.periods=seasonal.periods, p=p, q=q, tau=tau, control=list(maxit=(100*length(param.vector$vect)^2), parscale=par.scale))
} else {
optim.like <- optim(par=param.vector$vect, fn=calcLikelihoodNOTransformed, method="BFGS", opt.env=opt.env, x.nought=x.nought, use.beta=use.beta, use.small.phi=use.damping, seasonal.periods=seasonal.periods, p=p, q=q, tau=tau, control=list(parscale=par.scale))
}
#Get the parameters out of the param.vector
paramz <- unParameterise(optim.like$par, param.vector$control)
lambda <- paramz$lambda
alpha <- paramz$alpha
beta.v <- paramz$beta
small.phi <- paramz$small.phi
gamma.v <- paramz$gamma.v
ar.coefs <- paramz$ar.coefs
ma.coefs <- paramz$ma.coefs
##Calculate the variance:
#1. Re-set up the matrices
#w <- makeWMatrix(small.phi=small.phi, seasonal.periods=seasonal.periods, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
w <- .Call("makeBATSWMatrix", smallPhi_s = small.phi, sPeriods_s = seasonal.periods, arCoefs_s = ar.coefs, maCoefs_s = ma.coefs, PACKAGE = "forecast")
#g <- makeGMatrix(alpha=alpha, beta=beta.v, gamma.vector=gamma, seasonal.periods=seasonal.periods, p=p, q=q)
g <- .Call("makeBATSGMatrix", as.numeric(alpha), beta.v, gamma.v, seasonal.periods, as.integer(p), as.integer(q), PACKAGE="forecast")
F <- makeFMatrix(alpha=alpha, beta=beta.v, small.phi <- small.phi, seasonal.periods=seasonal.periods, gamma.bold.matrix=g$gamma.bold.matrix, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
#2. Calculate!
fitted.values.and.errors <- calcModel(y, x.nought, F, g$g, w)
e <- fitted.values.and.errors$e
fitted.values <- fitted.values.and.errors$y.hat
variance <- sum((e*e))/length(y)
}
#Get the likelihood
likelihood <- optim.like$value
#Calculate the AIC
aic <- likelihood+2*(length(param.vector$vect)+nrow(x.nought))
#Make a list object
model.for.output <- list(lambda=lambda, alpha=alpha, beta=beta.v, damping.parameter=small.phi, gamma.values=gamma.v, ar.coefficients=ar.coefs, ma.coefficients=ma.coefs, likelihood=likelihood, optim.return.code=optim.like$convergence, variance=variance, AIC=aic, parameters=list(vect=optim.like$par, control=param.vector$control), seed.states=x.nought, fitted.values=c(fitted.values), errors=c(e), x=fitted.values.and.errors$x, seasonal.periods=seasonal.periods, y=y)
class(model.for.output) <- "bats"
####
return(model.for.output)
}
calcModel <- function(y, x.nought, F, g, w) { #w is passed as a list
length.ts <- length(y)
x <- matrix(0, nrow=length(x.nought), ncol=length.ts)
y.hat <- matrix(0,nrow=1, ncol=length.ts)
e <- matrix(0, nrow=1, ncol=length.ts)
y.hat[,1] <- w$w.transpose %*% x.nought
e[,1] <- y[1]-y.hat[,1]
x[,1] <- F %*% x.nought + g %*% e[,1]
y <- matrix(y, nrow=1, ncol=length.ts)
loop <- .Call( "calcBATS", ys=y, yHats=y.hat, wTransposes = w$w.transpose, Fs=F, xs=x, gs=g, es=e, PACKAGE = "forecast" )
return(list(y.hat=loop$y.hat, e=loop$e, x=loop$x))
}
calcLikelihood <- function(param.vector, opt.env, use.beta, use.small.phi, seasonal.periods, p=0, q=0, tau=0, bc.lower=0, bc.upper=1) {
#param vector should be as follows: Box-Cox.parameter, alpha, beta, small.phi, gamma.vector, ar.coefs, ma.coefs
#Put the components of the param.vector into meaningful individual variables
box.cox.parameter <- param.vector[1]
alpha <- param.vector[2]
if(use.beta) {
if(use.small.phi) {
small.phi <- param.vector[3]
beta.v <- param.vector[4]
gamma.start <- 5
} else {
small.phi <- 1
beta.v <- param.vector[3]
gamma.start <- 4
}
} else {
small.phi <- NULL
beta.v <- NULL
gamma.start <- 3
}
if(!is.null(seasonal.periods)) {
gamma.vector <- param.vector[gamma.start:(gamma.start+length(seasonal.periods)-1)]
final.gamma.pos <- gamma.start+length(gamma.vector)-1
} else {
gamma.vector=NULL
final.gamma.pos <- gamma.start-1
}
if(p != 0) {
ar.coefs <- matrix(param.vector[(final.gamma.pos+1):(final.gamma.pos+p)], nrow=1, ncol=p)
} else {
ar.coefs <- NULL
}
if(q != 0) {
ma.coefs <- matrix(param.vector[(final.gamma.pos+p+1):length(param.vector)], nrow=1, ncol=q)
} else {
ma.coefs <- NULL
}
x.nought <- BoxCox(opt.env$x.nought.untransformed, lambda=box.cox.parameter)
#w <- makeWMatrix(small.phi=small.phi, seasonal.periods=seasonal.periods, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
#w <- .Call("makeBATSWMatrix", smallPhi_s = small.phi, sPeriods_s = seasonal.periods, arCoefs_s = ar.coefs, maCoefs_s = ma.coefs, PACKAGE = "forecast")
.Call("updateWtransposeMatrix", wTranspose_s=opt.env$w.transpose, smallPhi_s=small.phi, tau_s=as.integer(tau), arCoefs_s=ar.coefs, maCoefs_s=ma.coefs, p_s=as.integer(p), q_s=as.integer(q), PACKAGE = "forecast")
#g <- makeGMatrix(alpha=alpha, beta=beta, gamma.vector=gamma.vector, seasonal.periods=seasonal.periods, p=p, q=q)
#g <- .Call("makeBATSGMatrix", as.numeric(alpha), beta.v, gamma.vector, seasonal.periods, as.integer(p), as.integer(q), PACKAGE="forecast")
.Call("updateGMatrix", g_s=opt.env$g, gammaBold_s=opt.env$gamma.bold.matrix, alpha_s=alpha, beta_s=beta.v, gammaVector_s=gamma.vector, seasonalPeriods_s=seasonal.periods, PACKAGE="forecast")
#F <- makeFMatrix(alpha=alpha, beta=beta.v, small.phi=small.phi, seasonal.periods=seasonal.periods, gamma.bold.matrix=g$gamma.bold.matrix, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
.Call("updateFMatrix", opt.env$F, small.phi, alpha, beta.v, opt.env$gamma.bold.matrix, ar.coefs, ma.coefs, tau, PACKAGE="forecast")
mat.transformed.y <- BoxCox(opt.env$y, box.cox.parameter)
n <- ncol(opt.env$y)
.Call( "calcBATSFaster", ys=mat.transformed.y, yHats=opt.env$y.hat, wTransposes = opt.env$w.transpose, Fs=opt.env$F, xs=opt.env$x, gs=opt.env$g, es=opt.env$e, xNought_s = x.nought, sPeriods_s = seasonal.periods, betaV = beta.v, tau_s = as.integer(tau), p_s = as.integer(p), q_s = as.integer(q), PACKAGE = "forecast" )
log.likelihood <- n*log(sum(opt.env$e^2))-2*(box.cox.parameter-1)*sum(log(opt.env$y))
assign("D", (opt.env$F - opt.env$g %*% opt.env$w.transpose), envir=opt.env)
if(checkAdmissibility(opt.env, box.cox=box.cox.parameter, small.phi=small.phi, ar.coefs=ar.coefs, ma.coefs=ma.coefs, tau=tau, bc.lower=bc.lower, bc.upper=bc.upper)) {
return(log.likelihood)
} else {
return(10^20)
}
}
calcLikelihoodNOTransformed <- function(param.vector, opt.env, x.nought, use.beta, use.small.phi, seasonal.periods, p=0, q=0, tau=0) {
#The likelihood function without the Box-Cox Transformation
#param vector should be as follows: alpha, beta, small.phi, gamma.vector, ar.coefs, ma.coefs
#Put the components of the param.vector into meaningful individual variables
alpha <- param.vector[1]
if(use.beta) {
if(use.small.phi) {
small.phi <- param.vector[2]
beta.v <- param.vector[3]
gamma.start <- 4
} else {
small.phi <- 1
beta.v <- param.vector[2]
gamma.start <- 3
}
} else {
small.phi <- NULL
beta.v <- NULL
gamma.start <- 2
}
if(!is.null(seasonal.periods)) {
gamma.vector <- param.vector[gamma.start:(gamma.start+length(seasonal.periods)-1)]
final.gamma.pos <- gamma.start+length(gamma.vector)-1
} else {
gamma.vector=NULL
final.gamma.pos <- gamma.start-1
}
if(p != 0) {
ar.coefs <- matrix(param.vector[(final.gamma.pos+1):(final.gamma.pos+p)], nrow=1, ncol=p)
} else {
ar.coefs <- NULL
}
if(q != 0) {
ma.coefs <- matrix(param.vector[(final.gamma.pos+p+1):length(param.vector)], nrow=1, ncol=q)
} else {
ma.coefs <- NULL
}
#w <- makeWMatrix(small.phi=small.phi, seasonal.periods=seasonal.periods, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
#w <- .Call("makeBATSWMatrix", smallPhi_s = small.phi, sPeriods_s = seasonal.periods, arCoefs_s = ar.coefs, maCoefs_s = ma.coefs, PACKAGE = "forecast")
.Call("updateWtransposeMatrix", wTranspose_s=opt.env$w.transpose, smallPhi_s=small.phi, tau_s=as.integer(tau), arCoefs_s=ar.coefs, maCoefs_s=ma.coefs, p_s=as.integer(p), q_s=as.integer(q), PACKAGE = "forecast")
#g <- makeGMatrix(alpha=alpha, beta=beta, gamma.vector=gamma.vector, seasonal.periods=seasonal.periods, p=p, q=q)
#g <- .Call("makeBATSGMatrix", alpha, beta.v, gamma.vector, seasonal.periods, as.integer(p), as.integer(q), PACKAGE="forecast")
.Call("updateGMatrix", g_s=opt.env$g, gammaBold_s=opt.env$gamma.bold.matrix, alpha_s=alpha, beta_s=beta.v, gammaVector_s=gamma.vector, seasonalPeriods_s=seasonal.periods, PACKAGE="forecast")
#F <- makeFMatrix(alpha=alpha, beta=beta.v, small.phi=small.phi, seasonal.periods=seasonal.periods, gamma.bold.matrix=g$gamma.bold.matrix, ar.coefs=ar.coefs, ma.coefs=ma.coefs)
.Call("updateFMatrix", opt.env$F, small.phi, alpha, beta.v, opt.env$gamma.bold.matrix, ar.coefs, ma.coefs, tau, PACKAGE="forecast")
n <- ncol(opt.env$y)
#########################################################################################
#e <- calcModel(y=y, x.nought=x.nought, F=F, g=g$g, w=w)$e
######################
#### calcModel() code:
##
#x <- matrix(0, nrow=length(x.nought), ncol=n)
#y.hat <- matrix(0,nrow=1, ncol=n)
#e <- matrix(0, nrow=1, ncol=n)
#opt.env$y.hat[,1] <- w$w.transpose %*% x.nought
#opt.env$e[,1] <- opt.env$y[,1]-opt.env$y.hat[,1]
#opt.env$x[,1] <- opt.env$F %*% x.nought + g$g %*% opt.env$e[,1]
#mat.y <- matrix(opt.env$y, nrow=1, ncol=n)
.Call( "calcBATSFaster", ys=opt.env$y, yHats=opt.env$y.hat, wTransposes = opt.env$w.transpose, Fs=opt.env$F, xs=opt.env$x, gs=opt.env$g, es=opt.env$e, xNought_s = x.nought, sPeriods_s = seasonal.periods, betaV = beta.v, tau_s = as.integer(tau), p_s = as.integer(p), q_s = as.integer(q) , PACKAGE = "forecast" )
##
####
####################################################################
log.likelihood <- n*log(sum(opt.env$e*opt.env$e))
#D <- opt.env$F - g$g %*% w$w.transpose
assign("D", (opt.env$F - opt.env$g %*% opt.env$w.transpose), envir=opt.env)
if(checkAdmissibility(opt.env=opt.env, box.cox=NULL, small.phi=small.phi, ar.coefs=ar.coefs, ma.coefs=ma.coefs, tau=tau)) {
return(log.likelihood)
} else {
return(10^20)
}
}
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