Multi-step Time Series Forecasting with Machine Learning for Electricity Usage
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.
Machine learning algorithms predict a single value and cannot be used directly for multi-step forecasting. Two strategies that can be used to make multi-step forecasts with machine learning algorithms are the recursive and the direct methods.
In this tutorial, you will discover how to develop recursive and direct multi-step forecasting models with machine learning algorithms.
After completing this tutorial, you will know:
- How to develop a framework for evaluating linear, nonlinear, and ensemble machine learning algorithms for multi-step time series forecasting.
- How to evaluate machine learning algorithms using a recursive multi-step time series forecasting strategy.
- How to evaluate machine learning algorithms using a direct per-day and per-lead time multi-step time series forecasting strategy.
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.
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