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Putting Big Data analytics to work: Feature selection for forecasting electricity prices using the LASSO and random forests

descriptionPublicationkeyboard_double_arrow_right Article , Journal 02 Jan 2015Embargo end date: 01 Jan 2015 Switzerland, Germany Publisher:Informa UK LimitedJournal:Journal of Decision Systems, volume 24, pages 19-36 (issn: 1246-0125, eissn: 2116-7052,

Authors: Ludwig, Nicole; Feuerriegel, Stefan; Neumann, Dirk;

doi: 10.1080/12460125.2015.994290 , 10.3929/ethz-b-000182404

Putting Big Data analytics to work: Feature selection for forecasting electricity prices using the LASSO and random forests

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Abstract

Journal of Decision Systems, 24 (1)

Countries

Switzerland, Germany

Related Organizations

University of Freiburg
Germany
ETH Zurich
Switzerland

Keywords

decision support, exogenous predictors, electricity prices, predictive analytics, feature selection, predictive analytics; decision support; exogenous predictors; feature selection; electricity prices; weather data, weather data

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	95
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 1%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%