How to Develop a Skillful Machine Learning Time Series Forecasting Model

Last Updated on August 5, 2019

You are handed data and told to develop a forecast model.

What do you do?

This is a common situation; far more common than most people think.

• Perhaps you are sent a CSV file.
• Perhaps you are given access to a database.
• Perhaps you are starting a competition.

The problem can be reasonably well defined:

• You have or can access historical time series data.
• You know or can find out what needs to be forecasted.
• You know or can find out how what is most important in evaluating a candidate model.

So how do you tackle this problem?

Unless you have been through this trial by fire, you may struggle.

• You may struggle because you are new to the fields of machine learning and time series.
• You may struggle even if you have machine learning experience because time series data is different.
• You may struggle even if you have a background in time series forecasting because machine learning methods may outperform the classical approaches on your data.

In all of these cases, you will benefit from working through the problem carefully and systematically.

In this post, I want to give you a
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