Basic Feature Engineering With Time Series Data in Python

Last Updated on September 15, 2019

Time Series data must be re-framed as a supervised learning dataset before we can start using machine learning algorithms.

There is no concept of input and output features in time series. Instead, we must choose the variable to be predicted and use feature engineering to construct all of the inputs that will be used to make predictions for future time steps.

In this tutorial, you will discover how to perform feature engineering on time series data with Python to model your time series problem with machine learning algorithms.

After completing this tutorial, you will know:

  • The rationale and goals of feature engineering time series data.
  • How to develop basic date-time based input features.
  • How to develop more sophisticated lag and sliding window summary statistics features.

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 dive in.

  • Updated Jun/2017: Fixed a typo in the expanding window code example.
  • Updated Apr/2019: Updated the link to dataset.
  • Updated Aug/2019: Updated data loading to use new API.
  • Updated Sep/2019: Fixed bug in data loading.
To finish reading, please visit source site