Data augmentation for time series regression: Applying transformations, autoencoders and adversarial networks to electricity price forecasting

A model’s expected generalisation error is inversely proportional to its training set size. This relationship can pose a problem when modelling multivariate time series, because structural breaks, low sampling rates, and high data gathering costs can severely restrict training set sizes, increasing...

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Bibliographic Details
Published in:	Applied energy Vol. 304; p. 117695
Main Authors:	Demir, Sumeyra, Mincev, Krystof, Kok, Koen, Paterakis, Nikolaos G.
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 15.12.2021
Subjects:	Adversarial network Augmentation Autoencoder case studies electricity electricity costs Electricity price forecasting energy markets Multivariate time series prediction Regression regression analysis time series analysis Augmentation Adversarial network Electricity price forecasting Regression Multivariate time series Autoencoder
ISSN:	0306-2619, 1872-9118
Online Access:	Get full text
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