Machine Learning

A Gentle Introduction to SARIMA for Time Series Forecasting in Python

Last Updated on August 21, 2019

Autoregressive Integrated Moving Average, or ARIMA, is one of the most widely used forecasting methods for univariate time series data forecasting.

Although the method can handle data with a trend, it does not support time series with a seasonal component.

An extension to ARIMA that supports the direct modeling of the seasonal component of the series is called SARIMA.

In this tutorial, you will discover the Seasonal Autoregressive Integrated Moving Average, or SARIMA, method for time series forecasting with univariate data containing trends and seasonality.

After completing this tutorial, you will know:

  • The limitations of ARIMA when it comes to seasonal data.
  • The SARIMA extension of ARIMA that explicitly models the seasonal element in univariate data.
  • How to implement the SARIMA method in Python using the Statsmodels library.

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.

Update:  For help using and grid searching SARIMA hyperparameters, see this post:

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