I have 14 months(01/07/2018 to 30/08/2019) of one minute data, which I have aggregated to 10 mins block. So I have a data of dimension "61056 * 350". From this I am using 12 months of data to train the model and 2 months of data to validate it. I am using R version 3.6.0 to build my model.
I want to forecast the temperature for all 144 time points for next day using the data till previous day. Let's say I want to forecast temperature for 12/10/2019 (00:00 -23:59) using data till 10/10/2019 23:59. After building my model using data till 30/06/2019, how can I do that? I am using the following method to build my model.
library(data.table) library(tseries) library(astsa) library(forecast) #reading the dataset dataset<-fread(file = "/Project/data/dataset.csv") #unitroot test for the DV adf.test(dataset$DV,alternative = "stationary" , k= trunc((nrow(dataset)-1)^(1/3))) #gives strong evidence that the series is stationary #separating the training and testing dataset train<-dataset[as.Date(dataset$Time)<="2019-06-30",] test<-dataset[as.Date(dataset$Time)>"2019-07-01",] #converting the DV into a time series y<-ts(train$DV,start = c(2018,7),frequency = 24*6) #Fitting a tslm model to find out the important regressors for DV tslm_fit<-tslm(y~as.matrix(train[,2:350])) summary(tslm_fit) #converting the regressors into a matrix of time series xreg<-as.matrix(cbind(important vars selected from tslm fun)) names(xreg)<-names(variable names) #fitting an auto.arima model auto.arima(y,xreg = xreg,stepwise = F,allowdrift = F,trace = T) fit<-Arima(y,xreg = xreg, order = c(2,0,2), seasonal = c(1,0,0)) #Forecasting for the test data newregx<-as.matrix(cbind(same vars as train data)) pred<-forecast(fit,xreg = newxreg, h = nrow(newxreg), level = 97)
EDIT: Removed two questions from this thread.