Machine Learning

Time Series Prediction With Deep Learning in Keras

Last Updated on August 28, 2020

Time Series prediction is a difficult problem both to frame and to address with machine learning.

In this post, you will discover how to develop neural network models for time series prediction in Python using the Keras deep learning library.

After reading this post you will know:

  • About the airline passengers univariate time series prediction problem.
  • How to phrase time series prediction as a regression problem and develop a neural network model for it.
  • How to frame time series prediction with a time lag and develop a neural network model for it.

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 Oct/2016: Replaced graphs with more accurate versions.
  • Updated Mar/2017: Updated for Keras 2.0.2, TensorFlow 1.0.1 and Theano 0.9.0.
  • Updated Apr/2019: Updated the link to dataset.
  • Updated Sep/2019: Updated for Keras 2.2.5.

Problem Description

The problem we are going to look at in this post is the international airline passengers prediction problem.

This is a problem where given a year and a month, the task is to predict the number of
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