I work in the Oil & Gas industry.
I have been trying to build a ts forecasting model with covariates, and the model R code is as follows:
#Getting R libraries: library(readxl) library(ggplot2) library(forecast) library(timeSeries) library(tseries) library(MTS) #Create a time series object: myts <- ts(dataset, start = c(2005,1), end = c(2019,12), frequency = 12) #Illustrate out of sample forecasting with covariates, splitting the data: train <- window(myts, end = c(2018,12)) test <- window(myts, start = c(2019,1)) #Fitting the time series forecasting model: covariates <- c("Income","Prices","Sites","Vehicles") fit <- auto.arima(train[,"Volumes"], xreg = train[,covariates]) #Forecasting from test data: mytsfcast <- forecast(fit, h = 6*12, xreg = test[,covariates]) autoplot(mytsfcast)
However, I have been trying to forecast the retail volume sales 12,24,36, etc months out. The model only generates the following result:
Model fit results:
Please may I ask that you kindly advise on how I can get my model to forecast beyond end = c(2019,12). I am missing something?