We open this 48th issue of Foresight with an announcement of the goals and participation rules for the upcoming forecasting-methods competition, the M4. The M in all the competitions— the original, the M2, the M3, and now the M4—acknowledges their primary author, Spyros Makridakis. The competitions are extremely well-known events among forecasting researchers but are not really on the radar of most practitioners. The M4 promises results that practitioners should pay attention to as well.
In Part 2 of our four-part article on Forecasting the Impact of Artificial Intelligence, Spyros Makridakis examines four major scenarios for the social and economic impacts of AI and the actions needed to avoid the potentially negative consequences of these technologies.
Paul Goodwin, tells us How to Respond to a Forecasting Sceptic. Oliver Schaer and Simon Spavound provide a brief overview Paul’s new book Forewarned: A Sceptic’s Guide to Prediction.
Our traditional approach to sales forecasting, especially for large-scale product hierarchies, relies on time-series methods such as exponential smoothing, which account for trend and seasonal patterns in the historical data. But technological advances have made it feasible to incorporate external drivers of sales and thus improve sales-forecasting performance. Nikolaos Kourentzes and Yves Sagaert discuss the challenges of Incorporating Leading Indicators into Sales Forecasts and show how the task can be accomplished.
Our section on Collaborative Forecasting and Planning Practices contains the final installment of the three-part article by Chris Gray and John Dougherty entitled S&OP Misconceptions, Missteps, and Bad Practices. Their focus in Part 3 is on “Automating at the Expense of Judgment and Accountability.”
It seems like it’s every few months that we read about a new metric for evaluating forecast accuracy, and Foresight has attempted to keep up with the flow*. But a “pitfall to avoid” that has not been addressed in most books and software has to do with the forecasts from causal models, such as those using traditional regression and dynamic-regression methods. Drawing upon research in which he has participated, Foresight Editor, Len Tashman, shares his thoughts on causal models in Beware of Standard Prediction Intervals for Causal Models.
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*The Foresight Guidebook, Forecast Accuracy Measurement: Pitfalls to Avoid and Practices to Adopt (2010), offers a compendium of discussions on the subject by 15 authors, and an updated 2nd edition will be available in early 2018.