Automatic time series analysis for electric load forecasting via support vector regression

Maldonado, S. and González, A. and Crone, S. (2019) Automatic time series analysis for electric load forecasting via support vector regression. Applied Soft Computing Journal, 83. ISSN 1568-4946

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Abstract

In this work, a strategy for automatic lag selection in time series analysis is proposed. The method extends the ideas of feature selection with support vector regression, a powerful machine learning tool that can identify nonlinear patterns effectively thanks to the introduction of a kernel function. The proposed approach follows a backward variable elimination procedure based on gradient descent optimisation, iteratively adjusting the widths of an anisotropic Gaussian kernel. Experiments on four electricity demand forecasting datasets demonstrate the virtues of the proposed approach in terms of predictive performance and correct identification of relevant lags and seasonal patterns, compared to well-known strategies for time series analysis designed for energy load forecasting and state-of-the-art strategies for automatic model selection.

Item Type:
Journal Article
Journal or Publication Title:
Applied Soft Computing Journal
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1712
Subjects:
ID Code:
136026
Deposited By:
Deposited On:
24 Feb 2020 14:40
Refereed?:
Yes
Published?:
Published
Last Modified:
20 Oct 2020 07:36