Jason Brownlee

Neural networks like Long Short-Term Memory (LSTM) recurrent neural networks are able to almost seamlessly model problems with multiple input variables.

This is a great benefit in time series forecasting, where classical linear methods can be difficult to adapt to multivariate or multiple input forecasting problems.

In this tutorial, you will discover how you can develop an LSTM model for multivariate time series forecasting in the Keras deep learning library.

After completing this tutorial, you will know:

- How to transform a raw dataset into something we can use for time series forecasting.
- How to prepare data and fit an LSTM for a multivariate time series forecasting problem.
- How to make a forecast and rescale the result back into the original units.

Let’s get started.

Machine Learning Mastery

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