How to Make Out-of-Sample Forecasts with ARIMA in Python

Last Updated on August 28, 2019

Making out-of-sample forecasts can be confusing when getting started with time series data.

The statsmodels Python API provides functions for performing one-step and multi-step out-of-sample forecasts.

In this tutorial, you will clear up any confusion you have about making out-of-sample forecasts with time series data in Python.

After completing this tutorial, you will know:

• How to make a one-step out-of-sample forecast.
• How to make a multi-step out-of-sample forecast.
• The difference between the forecast() and predict() functions.

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 Apr/2019: Updated the link to dataset.
• Updated Aug/2019: Updated data loading to use new API.

How to Make Out-of-Sample Forecasts with ARIMA in Python
Photo by dziambel, some rights reserved.

Tutorial Overview

This tutorial is broken down into the following 5 steps:

1. Dataset Description
2. Split Dataset
3. Develop Model
4. One-Step Out-of-Sample Forecast
5. Multi-Step Out-of-Sample Forecast