[R] Help in Coding

Jeff Newmiller jdnewm|| @end|ng |rom dcn@d@v|@@c@@u@
Wed Oct 14 07:25:54 CEST 2020

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[1] https://stat.ethz.ch/pipermail/r-help/2020-October/469056.html

[2] http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example

[3] http://adv-r.had.co.nz/Reproducibility.html

[4] https://cran.r-project.org/web/packages/reprex/index.html (read the vignette) 

On October 13, 2020 8:42:08 PM PDT, Faheem Jan via R-help <r-help using r-project.org> wrote:
>Good morning dear administrators,
>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,s 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 iIneed help first how i 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.
>	[[alternative HTML version deleted]]
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Sent from my phone. Please excuse my brevity.

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