Machine Learning

Autoregression Models for Time Series Forecasting With Python

Last Updated on August 15, 2020

Autoregression is a time series model that uses observations from previous time steps as input to a regression equation to predict the value at the next time step.

It is a very simple idea that can result in accurate forecasts on a range of time series problems.

In this tutorial, you will discover how to implement an autoregressive model for time series forecasting with Python.

After completing this tutorial, you will know:

  • How to explore your time series data for autocorrelation.
  • How to develop an autocorrelation model and use it to make predictions.
  • How to use a developed autocorrelation model to make rolling predictions.

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 May/2017: Fixed small typo in autoregression equation.
  • Updated Apr/2019: Updated the link to dataset.
  • Updated Aug/2019: Updated data loading to use new API.
  • Updated Sep/2019: Updated examples to use latest plotting API.
  • Updated Apr/2020: Changed AR to AutoReg due to API change.
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