Hi, I am would like to ask few questions. I am trying to forecast hourly electricity prices by 24 hours ahead. I have hourly data starting from 2015*12*18 to 2017-10-24 and I have defined the data as time series as written in the code below.
Then I am trying do neural network with 23 non-seasonal dummies and 1 seasonal dummy. But I don’t know whether training set is enough.( Guess it is 50 hours in here?) The problem is that I couldn’t 24 for output here. How can I make such forecast? And my MASE score (6.95 in the Test set) is not good. Could be related to shortness of training set? The Code: library(zoo) library(readxl) setwd("C:/Users/emrek/Dropbox/2017-2018 Master Thesis/DATA") epias <- read_excel("eski.epias.xlsx") nPTF <- epias$`PTF (TL/MWh)` nSMF<- epias$`SMF(TL/MWh)` nC<- epias$`TT(MWh)` nEAK<- epias$`EAK-Toplam (MWh)` nTP<- epias$`Toplam (MWh)` times <- seq(from=as.POSIXct("2015-12-18 00:00:00"), to=as.POSIXct("2017-10-24 23:00:00"), by="hour") mydata <- rnorm(length(times)) PTF <- zoo(nPTF, order.by=times ) SMF <- zoo(nSMF, order.by=times ) C <- zoo(nC, order.by=times ) EAK <- zoo(nEAK, order.by=times ) TP<- zoo(nTP, order.by=times ) SH <- (EAK-TP) epias <- cbind(PTF,C,SH) View(epias) #neural networks library(forecast) set.seed(201) epias.nn <- nnetar(PTF, repeats = 50, p=23, P=1, size =12) summary(epias.nn$model[[1]]) epias.pred <- forecast(epias.nn, h= 24) accuracy(epias.pred, 24) plot(PTF, ylim=c(0,500) , ylab= , xlab= , bty="l", xaxt="n", xlim=c(as.POSIXct("2017-10-20 00:00:00"),as.POSIXct("2017-10-25 23:00:00")) , lty=1 ) lines(epias.pred$fitted,lwd = 2,col="blue") Best Regards, -- Emre [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.