Machine Learning

How to Make Predictions for Time Series Forecasting with Python

Last Updated on April 24, 2020

Selecting a time series forecasting model is just the beginning.

Using the chosen model in practice can pose challenges, including data transformations and storing the model parameters on disk.

In this tutorial, you will discover how to finalize a time series forecasting model and use it to make predictions in Python.

After completing this tutorial, you will know:

  • How to finalize a model and save it and required data to file.
  • How to load a finalized model from file and use it to make a prediction.
  • How to update data associated with a finalized model in order to make subsequent 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 Feb/2017: Updated layout and filenames to separate the AR case from the manual case.
  • Updated Apr/2019: Updated the link to dataset.
  • Updated Aug/2019: Updated CSV file loading.
  • Updated Apr/2020: Changed AR to AutoReg due to API change.
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