Through the M4 and M5 competitions, we've seen the promising performance of machine learning approaches in generating forecasts. The SAS whitepaper "Assisted Demand Planning Using Machine Learning for CPG and Retail" describes a role for ML in augmenting the demand planning by guiding the review and override of statistical forecasts.
Tag: forecast value added
In recent posts (March 26, April 21) we've looked at forecasting in the face of chaos and disruption. We've seen that traditional time series forecasting methods (used during "normal" times) can be creatively augmented with additional methods like clustering, similarity analysis, epidemiologic models, and simulation. While it is unreasonable to
Applying machine learning approaches to forecasting is an area of great research interest. Progress is being made on multiple fronts, for example: In the M4 Forecasting Competition, completed earlier this year, the top two performers utilized machine learning with traditional time series forecasting methods. At the link you'll find full
What is Forecast Value Added? Please enhance your Valentine's Day with this treat offered up by the Journal of Business Forecasting. Eric Wilson's very nice discussion of Forecast Value Added, originally published in the Spring 2016 issue of JBF, is now available online: "What is Forecast Value Added?" Eric also
Registration is now open for the SAS Analytics Experience 2017, being held September 18-20, in Washington, DC. (The Analytics Experience moves to Amsterdam, October 16-18 -- details on that event to follow.) For anyone interested in FVA analysis, Chip Wells and I will be delivering a half-day pre-conference training session
My colleague Gerhard Svolba (Solutions Architect at SAS Austria) has authored his third book, Applying Data Science: Business Case Studies Using SAS®." While the book covers a broad range of data science topics, forecasters will be particularly interested in two lengthy case studies on "Explaining Forecast Errors and Deviations" and
Typical Business Forecasting Process Let’s look at a typical business forecasting process. Historical data is fed into forecasting software which generates the "statistical" forecast. An analyst can review and override the forecast, which then goes into a more elaborate collaborative or consensus process for further adjustment. Many organizations also have
Journal of Business Forecasting columnist Larry Lapide is a longtime favorite of mine. As an industry analyst at AMR, and more recently as an MIT Research Affiliate, Larry's quarterly column is a perpetual source of guidance for the practicing business forecaster. No wonder he received IBF's 2012 Lifetime Achievement in
Last week I had the pleasure of attending (with six of my SAS colleagues) the IBF's Best Practices Forecasting Conference in Orlando. Some of the highlights: Charlie Chase and I were interviewed by Russell Goodman of SupplyChainBrain.com. The videos will be posted on SCB's website later this year. Meantime, enjoy
You may not be in London on October 7 to take advantage of the Lancaster Centre for Forecasting's free workshop on promotional forecasting. However, there are still plenty of forecasting educational opportunities coming up this fall: SAS Business Knowledge Series Best Practices in Demand-Driven Forecasting (Chicago, September 24-25) My colleague