A randomized-algorithm-based decomposition-ensemble learning methodology for energy price forecasting

Inspired by the interesting idea of randomization, some powerful but time-consuming decomposition-ensemble learning paradigms can be extended into extremely efficient and fast variants by using randomized algorithms as individual forecasting tools. In the proposed methodology, Three major steps, (1)...

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Veröffentlicht in:	Energy (Oxford) Jg. 157; S. 526 - 538
Hauptverfasser:	Tang, Ling, Wu, Yao, Yu, Lean
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Elsevier Ltd 15.08.2018 Elsevier BV
Schlagworte:	Algorithms artificial intelligence Big Data Computer applications Computing time Crude oil Decomposition Decomposition-ensemble learning methodology Econometrics energy costs Energy price forecasting Ensemble learning Extreme learning machine Forecasting Intelligence Learning algorithms Machine learning Mathematical models Natural gas Neural networks oils Parameter sensitivity prediction prices Random kitchen sinks Random vector functional link network Randomization Randomized algorithm Randomized algorithms Statistical analysis Statistical methods Random kitchen sinks Extreme learning machine Randomized algorithm Random vector functional link network Energy price forecasting Decomposition-ensemble learning methodology
ISSN:	0360-5442, 1873-6785
Online-Zugang:	Volltext
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