Time Series Prediction with LSTM Recurrent Neural Networks with Keras

Time series prediction problems are a difficult type of predictive modeling problem.

Unlike regression predictive modeling, time series adds the complexity of a sequence dependence among the input variables.

A powerful type of neural network designed to handle sequence dependence is called recurrent neural networks. The Long Short-Term Memory network or LSTM network is a type of recurrent neural network used in deep learning because very large architectures can be successfully trained.

Here you will discover how to develop LSTM networks in Python using the Keras deep learning library to address a demonstration time-series prediction problem.

By the end you will know how to implement and develop LSTM networks for your own time series prediction problems and other more general sequence problems. You will know:

• About the International Airline Passengers time-series prediction problem.
• How to develop LSTM networks for regression, window and time-step based framing of time series prediction problems?
• How to develop and make predictions using LSTM networks that maintain state (memory) across very long sequences?

Here we will develop a number of LSTMs for a standard time series prediction problem.

Source Code

Report

Next: Sequence Classification with LSTM Recurrent Neural Networks with Keras