Machine Learning

How to Model Residual Errors to Correct Time Series Forecasts with Python

Last Updated on April 24, 2020

The residual errors from forecasts on a time series provide another source of information that we can model.

Residual errors themselves form a time series that can have temporal structure. A simple autoregression model of this structure can be used to predict the forecast error, which in turn can be used to correct forecasts. This type of model is called a moving average model, the same name but very different from moving average smoothing.

In this tutorial, you will discover how to model a residual error time series and use it to correct predictions with Python.

After completing this tutorial, you will know:

  • About how to model residual error time series using an autoregressive model.
  • How to develop and evaluate a model of residual error time series.
  • How to use a model of residual error to correct predictions and improve forecast skill.

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