Guest blogger: Udo Sglavo on including R models in SAS Forecast Server (part 1 of 2)


My colleague Udo Sglavo is back, responding to comments on his guest blog from two weeks ago.  For fans of SAS and R, he shows how to incorporate results from Hyndman's R model into SAS.

Do the Evolution

After publishing my blog on replicating Rob J Hyndman’s  cross validation idea using SAS Forecast Server, some people were wondering about my statement: “SAS High-Performance Forecasting does not provide access to Hyndman’s ETS method; instead a multiplicative Winter’s method is used (for sake for the example).” SAS provides access to R through SAS/IML software, so shouldn’t it be possible to add R/ETS to a SAS High-Performance Forecasting model repository for comparison reasons?

Well, actually it is possible indeed – so I thought I should following up with an illustration featuring SAS High-Performance Forecasting (the batch engine of SAS Forecast Server).

While some people like to call it revolutionary if commercial vendors provide access to open source algorithms, I would like to consider it as evolutionary, as this has happened in other areas, such as data bases before. As you know SAS High-Performance Forecasting was designed with large-scale automatic forecasting challenges in mind. For automatic forecasting of large numbers of time series, only the most robust models should be used. The goal is to avoid making the analyst manually choose the best model for forecasting each time series. However, SAS High-Performance Forecasting does allow analysts to include their own forecasting modeling algorithms. We like to distinguish between:

  • Custom repositories: You can create your own SAS model repository.
  • External models: Your forecasts are provided by methods that are external to the SAS.
  • User-defined models: You are adding forecasting methods that are not provided by SAS High-Performance Forecasting.

For details see my white paper: “Extending SAS® High-Performance Forecasting Using User-Defined Models and External Forecasts”.

I have to admit that my R coding skills are extremely poor and there might be room for improvement (and I invite you to send me your suggestions). But conceptually, you might find the code (in Part 2) to be a useful starting point.


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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