Announcing: SAS Forecast Server 4.1


Tuesday's release of SAS 9.3 included the new SAS Forecast Server 4.1, which has several valuable enhancements:

Combination (Ensemble) Models: A combination of forecasts using different forecasting techniques can outperform forecasts produced by using any single technique. Users can combine forecasts produced by many different models using several different combination techniques. (See section 12. Combining Forecasts (p. 24ff) on the Forecasting Principles website.)

Rolling Simulations: A sophisticated tool for iteratively simulating the forecasting process over a holdout period. Allows you to evaluate the stability and performance of a model, helping you choose the most appropriate model for forecasting the future. (See previous The BFD blog posting on Holdout Sets: Good or Bad.)

Temporal Reconciliation: A powerful new tool for combining and reconciling forecasts generated at different time frequencies (e.g. hourly, daily, weekly). Consider demand forecasting in the electric utility industry, where consumption varies by hour within the day (less demand in the middle of the night), by day within the week (business days compared to weekends), and by week within the year (higher peak demand in summer to drive air conditioners). With the SAS Forecast Server Procedures engine inside SAS Forecast Server 4.1, you can model the behavior at each time frequency, and then combine the results to incorporate the various seasonal cycles.

Custom Time Intervals: Some organizations use non-standard planning calendars. Other times there are gaps in the data (such as periods of the year (out of season) where a product is not sold). Custom time intervals can be used to model data at a frequency that is familiar to the business, and to eliminate gaps by compressing data. (See an example of when you would use this approach in the SAS whitepaper "Forecasting by Time Compression" by my colleague, Udo Sglavo.)

SAS Forecasting on YouTube

This week we also released a brief (2 minute) SAS Forecasting video on YouTube.

I lobbied hard for a role as the SAS spokesperson, and was one of 12 employees invited to audition. Apparently, my ability to emote was not sufficient to overcome numerous other shortcomings, and I failed to land the part. As one of the producers commented about my audition, "Nice delivery, but he has a face for podcasting."


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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