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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