Forecast with STL and Regressors Using R Package Forecast – How to Execute

forecastingrregression

I would like to use the stlf forecast function from the R package forecast (http://cran.r-project.org/web/packages/forecast/index.html) and include regressors in the model.

Question 1: This can only be done using "" method="arima" "" – right?

Question 2: For the model calibration the parameter "xreg" should work but how can I enter the new data for th forecast? "newxreg" did not work for me.

Thank you

Best Answer

The forecast.stl() function does not allow for regressors. If you include an xreg argument with method="arima", it will be used in the fitted model, but not in the forecasts. You could modify the forecast.stl() function to handle this.

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Solved – Exogeneous regressors in auto.arima and using them in forecast function in R

If you fit an arima model with external regressions, you MUST provide newxreg to the predictions function. This goes for arima, auto.arima, and Arima. You cannot provide external regressors to ets.

xreg should contain the exogenous variables for the training set, and newxreg should contain those variables for the prediction set. If the nexreg contains variables you do not know the future values of, you will either need to forecast these variables or exclude them from the model.

Solved – Performing a time series ARIMA model on natural gas power demand using the forecast package from R

Unfortunately you have few technical errors here.

You cannot make ARIMAX-model with library(forecast) function auto.arima. Xreg argument makes it regression model with ARMA errors. That is something which I had to learn hard way by wondering the results... :)

And you have to supply FUTURE values for the xreg argument in the forecast-function. Split your data into two parts: 1) one to fit model 2) future values for the exogenous variables. Auto.arima does not forecast future values of xreg variables by ARIMA-models.

If you want to try ARIMAX-models try library(TSA) with arimax-function which of course has different syntax than auto.arima - function... :)

EDIT:

Here is example for using auto.arima with xreg argument with data set having first data for model parameters estimation and then for forecasting.

library(forecast);  
apu=read.table("demo_1.csv",sep=";",dec=",",header=TRUE);  
apux=read.table("demo_2.csv",sep=";",dec=",",header=TRUE);  
apuxx=read.table("demo_3_xreg.csv",sep=";",dec=",",header=TRUE);  
apu2=ts(data=apu[2],start=c(2011,1),deltat=1/365);  
apu3=ts(data=apu[3],start=c(2011,1),deltat=1/365);  
apu4=ts(data=apux[1],start=c(2011,1),deltat=1/365);  
Acf(apu2);  
Pacf(apu2);  
apu5=ts.intersect(apu3,apu4);  
apu6=ts(data=apuxx[3],start=c(2013,263),deltat=1/365);  
apu7=ts(data=apuxx[2],start=c(2013,263),deltat=1/365);  
apu8=ts.intersect(apu6,apu7);   


sarimax=auto.arima(apu2, d=NA, D=NA, max.p=5, max.q=5,  
     max.P=365, max.Q=365, max.order=5, start.p=2, start.q=2,  
     start.P=1, start.Q=1, stationary=FALSE, seasonal=TRUE,  
     ic=c("aicc","aic", "bic"), stepwise=TRUE, trace=FALSE,  
     approximation=(length(apu2)>100 | frequency(apu2)>12), xreg=apu5,  
     test=c("kpss","adf","pp"), seasonal.test=c("ocsb","ch"),  
     allowdrift=TRUE, lambda=0, parallel=FALSE, num.cores=NULL);  
print(sarimax$arma);  
    print(accuracy(sarimax));  
    print(sarimax$coef);  
plot(sarimax$residuals);    
    print(Box.test(sarimax$residuals,lag=30,type=c("Ljung-Box")));    

sarimaxpredicts=forecast(sarimax, h=7,level=c(75,80,90,95), fan=FALSE, xreg=apu8,  lambda=sarimax$lambda,bootstrap=FALSE, npaths=5000);  
plot(sarimaxpredicts);

Best Answer

Related Solutions

Solved – Exogeneous regressors in auto.arima and using them in forecast function in R

Solved – Performing a time series ARIMA model on natural gas power demand using the forecast package from R

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