How to Remove Trends and Seasonality with a Difference Transform in Python

Last Updated on June 23, 2020

Time series datasets may contain trends and seasonality, which may need to be removed prior to modeling.

Trends can result in a varying mean over time, whereas seasonality can result in a changing variance over time, both which define a time series as being non-stationary. Stationary datasets are those that have a stable mean and variance, and are in turn much easier to model.

Differencing is a popular and widely used data transform for making time series data stationary.

In this tutorial, you will discover how to apply the difference operation to your time series data with Python.

After completing this tutorial, you will know:

• The contrast between a stationary and non-stationary time series and how to make a series stationary with a difference transform.
• How to apply the difference transform to remove a linear trend from a series.
• How to apply the difference transform to remove a seasonal signal from a series.

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