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.
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
Look at the following graphs showing one weekly time series. The left hand shows the actual time series plot. To the less statistically inclined this plot might indicate that the data is seasonal due to the troughs during summer and the peaks during winter. However, if you were to use
In this guest blog, my colleague Snurre Jensen (Analytic Solutions Manager, SAS EMEA Technology Practice) discusses a fine point regarding the word “seasonality” in time series forecasting. When we see general ups and downs in the data that tend to repeat year after year, we commonly refer to this as
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
In this guest blogger post, Udo Sglavo of the Advanced Analytics Division of SAS shows how to conduct time series cross-validation using SAS Forecast Server. Udo replicates the example from Rob J Hyndman's Research Tips blog. Replicating the Example In order to replicate the example in Hyndman's blog, the example
The hurricane didn't get me, but Monday night's thunderstorm sure played a dirty trick. After leaving my car windows cracked open over night, I drove to work Tuesday morning feeling a little soggier by the minute. Upon arrival at SAS, I was aghast to find the seat of my pants was soaked
We had a tornado in April, an earthquake on Tuesday, a drought all summer, and a hurricane arrives on Saturday. All I can figure is that Cary, NC has way too many sinners per capita. What's next -- pestilence? The BFD Makeover The BFD (and all SAS blogs) will now be
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
No, BONEZONE is not the website of wayward legislators. It is, however, a trade journal of the orthopaedic devices industry, and the Summer 2011 issue features a nice mention of Forecast Value Added (FVA) analysis in an article by Tom Wallace. In "Forecasting: It's Getting Better," Tom refers to FVA
There is nothing the gambler, investor, forecaster, or Match.com dater likes as much as the sure thing. Don't we all? Back in April I stated what I claimed to be a sure thing forecast: In any group of 2 or more people, there is at least one pair of people
