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 details on the rules, the 100,000 time series used in the competition, and results.
- At the SAS Analytics Experience 2018 in San Diego last month, SAS customer Kellogg's along with SAS partner First Analytics reported on a machine learning approach to guide overrides of statistical forecasts to improve accuracy. (Register here to watch their Tuesday September 18 presentation "How Machine Learning Boosts Statistical Forecasting for Better Demand Planning at Kellogg’s" on demand.)
And on Tuesday October 23, noon-1:00pm ET, SAS is presenting a live webinar:
How Demand Planning Will Benefit From Machine Learning
My colleagues Varun Valsaraj, Becky Gallagher, and Charlie Chase present their approach to enhancing business forecasting with machine learning. Their novel approach helps demand planners identify which forecasts should be manually adjusted, and provides guidance on the direction and size of the adjustment. This method shows promise at reducing the number of overrides made by planners (saving time), and improving the quality of the overrides (increasing Forecast Value Added).
Register here for the free live webinar, and check back for the on-demand recording.