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

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…


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