How to Configure Multilayer Perceptron Network for Time Series Forecasting

Last Updated on August 28, 2020

It can be difficult when starting out on a new predictive modeling project with neural networks.

There is so much to configure, and no clear idea where to start.

It is important to be systematic. You can break bad assumptions and quickly hone in on configurations that work and areas for further investigation likely to payoff.

In this tutorial, you will discover how to use exploratory configuration of multilayer perceptron (MLP) neural networks to find good first-cut models for time series forecasting.

After completing this tutorial, you will know:

• How to design a robust experimental test harness to evaluate MLP models for time series forecasting.
• Systematic experimental designs for varying epochs, neurons, and lag configurations.
• How to interpret results and use diagnostics to learn more about well-performing models.

Kick-start your project with my new book Deep Learning for Time Series Forecasting, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

• Updated Jul/2017: Changed function for creating models to be more descriptive.
• Updated Apr/2019: Updated the link to dataset.

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