How to Identify and Remove Seasonality from Time Series Data with Python

Last Updated on August 15, 2020

Time series datasets can contain a seasonal component.

This is a cycle that repeats over time, such as monthly or yearly. This repeating cycle may obscure the signal that we wish to model when forecasting, and in turn may provide a strong signal to our predictive models.

In this tutorial, you will discover how to identify and correct for seasonality in time series data with Python.

After completing this tutorial, you will know:

• The definition of seasonality in time series and the opportunity it provides for forecasting with machine learning methods.
• How to use the difference method to create a seasonally adjusted time series of daily temperature data.
• How to model the seasonal component directly and explicitly subtract it from observations.

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 link to dataset.
• Updated Aug/2019: Updated data loading to use new API.

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