[R] Help in R code

Faheem Jan |@heemj@n93 @end|ng |rom y@hoo@com
Sun Oct 18 11:56:44 CEST 2020

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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