Good morning, Please help me to code this code in R.
I working in the multivariate time series data, know my objective is that to
one year forecast of the hourly time series data, using first five as a
training set and the remaining one year as validation. For this I transform
the the data into functional data through Fourier basis functional, apply
functional principle components as dimensional reduction explaining a specific
amount of variation , using the corresponding functional principle
components scores. I use the VAR model on those FPCscores for forecasting one
day ahead forecast, know my problem is that i choose four Fpc scores which give
only four value in a single day, I want the forecast for 24 hours not only 4,
and then i want to transform it back to the original functional data. for the
understanding i am sharing my code (1) transform of the multivariate time
series data in functional data(2) the functional principle components and the
corresponding scores(3) I use functional final prediction error for the
selection of the parameters on the VAR model(4) Using VAR for the analysis and
forecasting .(1) nb = 23 # number of basis functions for the data fbf =
create.fourier.basis(rangeval=c(0,1), nbasis=nb) # basis for data
args=seq(0,1,length=24) fdata1=Data2fd(args,y=t(mat),fbf) # functions
generated from discretized y(2) ffpe = fFPE(fdata1, Pmax=10) d.hat = ffpe[1]
#order of the model p.hat = ffpe[2] #lag of the model
(3) n = ncol(fdata1$coef) D = nrow(fdata1$coef) #center the data mu =
mean.fd(fdata1) data = center.fd(fdata1) #fPCA fpca = pca.fd(data,nharm=D)
scores = fpca$scores[,1:d.hat](4) # to avoid warnings from vars predict
function below colnames(scores) <- as.character(seq(1:d.hat))
VAR.pre= predict(VAR(scores, p.hat), n.ahead=1, type="const")$fcst
after this I need help first how to transform this into original Functional
data and to obtain the for for each 24 hours (mean one day forecast) and to how
to generalize the result for one year.
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