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Mike Gilliland
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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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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

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The New (BF) Deal

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

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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

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Spring Forecasting Events

We're having an early spring in North Carolina. Trees are budding, flowers are blooming, and the warmer temperatures make even a pistol whipping more enjoyable. What better way to take advantage of the new season than filling your spring with educational opportunities in forecasting. Plan in Perfect Sync with Customer

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When Executive Managment Hurts

As we discussed last week, the forecasting process is often contaminated by individuals whose input makes the forecast worse. Sometimes this is intentional. For example, if I'm tired of hearing customers complain about out-of-stocks on retail shelves, I'll try to drive up the forecast so that more inventory will be

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Forecasting or Golf?

A recurring theme of The Business Forecasting Deal (both this blog and the book) is that forecasting is a huge waste of management time. This doesn't mean that forecasting is pointless, irrelevant, or entirely useless in running our organizations. It only means that the amount of time, money, and human

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In Defense of Outliers

If outliers could scream, would we be so cavalier about removing them from our history, and excluding them from our statistical forecasting models? Well, maybe we would – if they screamed all the time, and for no good reason. (This sentiment is adapted from my favorite of the many Deep

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Mistakes in the Forecasting Hierarchy

Many forecasting software packages support hierarchical forecasting. You define the hierarchical relationship of your products and locations, create forecasts at one or more levels, and then reconcile the forecasts across the full hierarchy. In a top-down approach, you generate forecasts at the highest level and apportion it down to lower

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