Machine Learning

How to Make Manual Predictions for ARIMA Models with Python

Last Updated on August 28, 2019

The autoregression integrated moving average model or ARIMA model can seem intimidating to beginners.

A good way to pull back the curtain in the method is to to use a trained model to make predictions manually. This demonstrates that ARIMA is a linear regression model at its core.

Making manual predictions with a fit ARIMA models may also be a requirement in your project, meaning that you can save the coefficients from the fit model and use them as configuration in your own code to make predictions without the need for heavy Python libraries in a production environment.

In this tutorial, you will discover how to make manual predictions with a trained ARIMA model in Python.

Specifically, you will learn:

  • How to make manual predictions with an autoregressive model.
  • How to make manual predictions with a moving average model.
  • How to make predictions with an autoregression integrated moving average model.

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Let’s dive in.