Convolutional Neural Networks for Multi-Step Time Series Forecasting
Last Updated on August 28, 2020
Given the rise of smart electricity meters and the wide adoption of electricity generation technology like solar panels, there is a wealth of electricity usage data available.
This data represents a multivariate time series of power-related variables that in turn could be used to model and even forecast future electricity consumption.
Unlike other machine learning algorithms, convolutional neural networks are capable of automatically learning features from sequence data, support multiple-variate data, and can directly output a vector for multi-step forecasting. As such, one-dimensional CNNs have been demonstrated to perform well and even achieve state-of-the-art results on challenging sequence prediction problems.
In this tutorial, you will discover how to develop 1D convolutional neural networks for multi-step time series forecasting.
After completing this tutorial, you will know:
- How to develop a CNN for multi-step time series forecasting model for univariate data.
- How to develop a multichannel multi-step time series forecasting model for multivariate data.
- How to develop a multi-headed multi-step time series forecasting model for multivariate data.
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.