Measuring Forecast Accuracy

Forecast evaluation statistics with examples in Python

If I had to choose one basic skill in data science that is the most useful, it would be time series forecasting. Predicting the future value of something contributes to making better decisions. Therefore, it is crucial to be sure that you can rely on forecasting. The choosing, construction and interpretation of forecast accuracy metrics are just as important as making forecasts.

What actually makes up the accuracy of the forecast?

Unless stated otherwise, all pictures in the article are by the author.

How to choose evaluation statistics

Choosing a accuracy estimation method often depends on the domain of the problem. In my career, I have encountered a situation where a hastily chosen metric has caused the client’s dissatisfaction with the forecasting results optimized for KPI inconsistent with the specific business case. For example, the model may have a low mean square error, but at the same time doesn’t predict sudden deviations from “everyday normal” values or trend changes.

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This article will show you the fundamental forecast evaluation statistics that you can use to build and test your predictive models. We will calculate and interpret them using concrete examples: the mean…