A Gentle Introduction to the Box-Jenkins Method for Time Series Forecasting

Last Updated on August 15, 2020

The Autoregressive Integrated Moving Average Model, or ARIMA for short is a standard statistical model for time series forecast and analysis.

Along with its development, the authors Box and Jenkins also suggest a process for identifying, estimating, and checking models for a specific time series dataset. This process is now referred to as the Box-Jenkins Method.

In this post, you will discover the Box-Jenkins Method and tips for using it on your time series forecasting problem.

Specifically, you will learn:

• About the ARIMA process and how the 3 steps of the Box-Jenkins Method.
• Best practice heuristics for selecting the q, d, and p model configuration for an ARIMA model.
• Evaluating models by looking for overfitting and residual errors as a diagnostic process.

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