How to Decompose Time Series Data into Trend and Seasonality

Last Updated on August 14, 2020

Time series decomposition involves thinking of a series as a combination of level, trend, seasonality, and noise components.

Decomposition provides a useful abstract model for thinking about time series generally and for better understanding problems during time series analysis and forecasting.

In this tutorial, you will discover time series decomposition and how to automatically split a time series into its components with Python.

After completing this tutorial, you will know:

• The time series decomposition method of analysis and how it can help with forecasting.
• How to automatically decompose time series data in Python.
• How to decompose additive and multiplicative time series problems and plot the results.

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.