How to Grid Search SARIMA Hyperparameters for Time Series Forecasting
Last Updated on August 28, 2020
The Seasonal Autoregressive Integrated Moving Average, or SARIMA, model is an approach for modeling univariate time series data that may contain trend and seasonal components.
It is an effective approach for time series forecasting, although it requires careful analysis and domain expertise in order to configure the seven or more model hyperparameters.
An alternative approach to configuring the model that makes use of fast and parallel modern hardware is to grid search a suite of hyperparameter configurations in order to discover what works best. Often, this process can reveal non-intuitive model configurations that result in lower forecast error than those configurations specified through careful analysis.
In this tutorial, you will discover how to develop a framework for grid searching all of the SARIMA model hyperparameters for univariate time series forecasting.
After completing this tutorial, you will know:
- How to develop a framework for grid searching SARIMA models from scratch using walk-forward validation.
- How to grid search SARIMA model hyperparameters for daily time series data for births.
- How to grid search SARIMA model hyperparameters for monthly time series data for shampoo sales, car sales, and temperature.
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