Machine Learning

How to Grid Search ARIMA Model Hyperparameters with Python

Last Updated on August 28, 2019

The ARIMA model for time series analysis and forecasting can be tricky to configure.

There are 3 parameters that require estimation by iterative trial and error from reviewing diagnostic plots and using 40-year-old heuristic rules.

We can automate the process of evaluating a large number of hyperparameters for the ARIMA model by using a grid search procedure.

In this tutorial, you will discover how to tune the ARIMA model using a grid search of hyperparameters in Python.

After completing this tutorial, you will know:

  • A general procedure that you can use to tune the ARIMA hyperparameters for a rolling one-step forecast.
  • How to apply ARIMA hyperparameter optimization on a standard univariate time series dataset.
  • Ideas for extending the procedure for more elaborate and robust models.

Kick-start your project with my new book Time Series Forecasting With Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

  • Updated Apr/2019: Updated the links to datasets.
  • Updated Aug/2019: Updated data loading to use new API.
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