Forecasting carbon futures price: a hybrid method incorporating fuzzy entropy and extreme learning machine

In this paper, we propose a novel hybrid model that extends prior work involving ensemble empirical mode decomposition (EEMD) by using fuzzy entropy and extreme learning machine (ELM) methods. We demonstrate this 3-stage model by applying it to forecast carbon futures prices which are characterized...

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Veröffentlicht in:	Annals of operations research Jg. 313; H. 1; S. 559 - 601
Hauptverfasser:	Chen, Peng, Vivian, Andrew, Ye, Cheng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.06.2022 Springer Springer Nature B.V
Schlagworte:	Algorithms Artificial neural networks Business and Management Carbon Cluster analysis Clustering Combinatorics Emissions credit trading Entropy Forecasting Futures Low frequencies Machine learning Mathematical models Operations research Operations Research/Decision Theory Original Research Pricing Reconstruction Residues Theory of Computation Vector quantization China Carbon futures price Fuzzy entropy ARMA Extreme learning machine K-means clustering method EEMD
ISSN:	0254-5330, 1572-9338
Online-Zugang:	Volltext
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