How to Convert a Time Series to a Supervised Learning Problem in Python
Last Updated on August 21, 2019
Machine learning methods like deep learning can be used for time series forecasting.
Before machine learning can be used, time series forecasting problems must be re-framed as supervised learning problems. From a sequence to pairs of input and output sequences.
In this tutorial, you will discover how to transform univariate and multivariate time series forecasting problems into supervised learning problems for use with machine learning algorithms.
After completing this tutorial, you will know:
- How to develop a function to transform a time series dataset into a supervised learning dataset.
- How to transform univariate time series data for machine learning.
- How to transform multivariate time series data for machine learning.
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.
How to Convert a Time Series to a Supervised Learning Problem in Python
Photo by Quim Gil, some rights reserved.