Time Series Forecasting Performance Measures With Python

Last Updated on September 10, 2020

Time series prediction performance measures provide a summary of the skill and capability of the forecast model that made the predictions.

There are many different performance measures to choose from. It can be confusing to know which measure to use and how to interpret the results.

In this tutorial, you will discover performance measures for evaluating time series forecasts with Python.

Time series generally focus on the prediction of real values, called regression problems. Therefore the performance measures in this tutorial will focus on methods for evaluating real-valued predictions.

After completing this tutorial, you will know:

• Basic measures of forecast performance, including residual forecast error and forecast bias.
• Time series forecast error calculations that have the same units as the expected outcomes such as mean absolute error.
• Widely used error calculations that punish large errors, such as mean squared error and root mean squared error.

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• Jun/2019: Fixed typo in forecast bias (thanks Francisco).

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