How to Grid Search Deep Learning Models for Time Series Forecasting

Last Updated on August 28, 2020

Grid searching is generally not an operation that we can perform with deep learning methods.

This is because deep learning methods often require large amounts of data and large models, together resulting in models that take hours, days, or weeks to train.

In those cases where the datasets are smaller, such as univariate time series, it may be possible to use a grid search to tune the hyperparameters of a deep learning model.

In this tutorial, you will discover how to develop a framework to grid search hyperparameters for deep learning models.

After completing this tutorial, you will know:

• How to develop a generic grid searching framework for tuning model hyperparameters.
• How to grid search hyperparameters for a Multilayer Perceptron model on the airline passengers univariate time series forecasting problem.
• How to adapt the framework to grid search hyperparameters for convolutional and long short-term memory neural networks.

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.

• Update May/2019: Fixed small double assignment issue in the code (thanks Jameson).