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.
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