Guest Blogger: Udo Sglavo on Cross-validation using SAS Forecast Server (Part 2 of 2)


In Part 1, Udo provided SAS code to replicate the example in Hyndman's blog.  Below, he shows the results of out-of-sample testing, and draws some conclusions on the computational efficiency of this approach.

Out-of-sample Testing

In addition to the example shared by Hyndman, out-of-sample data was used to illustrate the final performance of the winning model. The last 12 month of A10 were used as out-of-sample data, which changes the MAE plot above, as less data is available for cross-validation (again, ARIMA is the preferred model):

The new cross validation plot:

Forecast plot:

Forecast region only plot:



Time series cross-validation as suggested by Hyndman can be implemented using SAS High-Performance Forecasting today. Cross-validation is a computationally intensive approach to assessing forecast model performance, so this needs to be taken into account when trying to apply it on large scale data. Nevertheless, each iteration of cross-validation (for each lead time) can be run independently, so a multi-threaded approach might improve the overall run-time (by running on several processors in parallel). It should also be noted that in this example the forecasting models candidates are already known, which might not be the case in large-scale environments for all series at hand.


About Author

Mike Gilliland

Product Marketing Manager

Michael Gilliland is a longtime business forecasting practitioner and formerly a Product Marketing Manager for SAS Forecasting. He is on the Board of Directors of the International Institute of Forecasters, and is Associate Editor of their practitioner journal Foresight: The International Journal of Applied Forecasting. Mike is author of The Business Forecasting Deal (Wiley, 2010) and former editor of the free e-book Forecasting with SAS: Special Collection (SAS Press, 2020). He is principal editor of Business Forecasting: Practical Problems and Solutions (Wiley, 2015) and Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning (Wiley, 2021). In 2017 Mike received the Institute of Business Forecasting's Lifetime Achievement Award. In 2021 his paper "FVA: A Reality Check on Forecasting Practices" was inducted into the Foresight Hall of Fame. Mike initiated The Business Forecasting Deal blog in 2009 to help expose the seamy underbelly of forecasting practice, and to provide practical solutions to its most vexing problems.

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