Machine Learning

How to Check if Time Series Data is Stationary with Python

Last Updated on August 15, 2020

Time series is different from more traditional classification and regression predictive modeling problems.

The temporal structure adds an order to the observations. This imposed order means that important assumptions about the consistency of those observations needs to be handled specifically.

For example, when modeling, there are assumptions that the summary statistics of observations are consistent. In time series terminology, we refer to this expectation as the time series being stationary.

These assumptions can be easily violated in time series by the addition of a trend, seasonality, and other time-dependent structures.

In this tutorial, you will discover how to check if your time series is stationary with Python.

After completing this tutorial, you will know:

  • How to identify obvious stationary and non-stationary time series using line plot.
  • How to spot check summary statistics like mean and variance for a change over time.
  • How to use statistical tests with statistical significance to check if a time series is stationary.

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.