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.
This tutorial is divided into 3 parts; they are:
Air Pollution Forecasting
Basic Data Preparation
Multivariate LSTM Forecast Model
This tutorial assumes you have a Python SciPy environment installed. You can use either Python 2 or 3 with this tutorial.
You must have Keras (2.0 or higher) installed with either the TensorFlow or Theano backend.
The tutorial also assumes you have scikit-learn, Pandas, NumPy and Matplotlib installed.
If you need help with your environment, see this post:
How to Setup a Python Environment for Machine Learning and Deep Learning with Anaconda