Aug 27 2021
My last two long posts were about evaluating sales forecasts. They begged the question of how you generate these forecasts. This is a partial answer, about what you can tell from a history of sales through both classical methods and recent developments, particularly probability forecasting.
Jul 30 2021
The lively response to last week’s post on this topic prompted me to dig deeper. First, I take a shot at clarifying the distinction between point forecasts and probability forecasts. Second, I present the idea behind the accuracy metric for probability forecasts that Stefan de Kok recommends as an alternative to the WSPL. Finally, I summarize a few points raised in discussions on LinkedIn and in this blog.
All of this is about evaluating forecasts. We still need methods to generate them. There are many well-known, published methods for point forecasts but not for probability forecasts, particularly for sales. This is a topic for another post.
Oct 8 2021
Sales Forecasts – Part 4. Generating Point Forecasts with Trends and Seasonality
This fourth post about sales forecasts addresses what you actually start with — that is, visualizing the time series of historical sales and generating point estimates for the future. Theyou analyze the residuals to determine the probability forecasts.
What prompted me to review this field is the realization based on news of the M5 forecasting competition that this field has been the object of intense developments in recent years. Some techniques from earlier decades are now accessible through open-source software that can crunch tens of thousands of data points on an ordinary office laptop.
Others are new developments. Thanks to Stefan de Kok, John Darlington, Nicolas Vandeput, and Bill Waddell for comments and questions on the previous posts, that made me dig deeper:
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By Michel Baudin • Tools • 0 • Tags: Exponential Smoothing, Holt-Winters, Point Forecast, Probability Forecast, Sales Forecasting, Time Series