Summary

In this chapter, we looked at the thickly populated and highly controversial area of forecast metrics. We started with a basic taxonomy of forecast measures to help you categorize and organize all the metrics in the field.

Then, we launched a few experiments through which we learned about the different properties of these metrics, slowly approaching a better understanding of what these metrics are measuring, but looking at synthetic time series experiments, we learned how MAPE and sMAPE favor under- and over-forecasting, respectively.

We also analyzed the rank correlations between these metrics on real data to see how similar the different metrics are and finally, rounded off by laying out a few guidelines that can help you pick a forecasting metric for your problem.

In the next chapter, we will look at cross-validation strategies for time series.