Carbon price forecasting based on modified ensemble empirical mode decomposition and long short-term memory optimized by improved whale optimization algorithm

The accurate prediction of carbon prices poses a tremendous challenge to relevant industry practitioners and governments. This paper proposes a novel hybrid model incorporating modified ensemble empirical mode decomposition (MEEMD) and long short-term memory (LSTM) optimized by the improved whale op...

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Vydáno v:	The Science of the total environment Ročník 716; s. 137117
Hlavní autoři:	Yang, Shaomei, Chen, Dongjiu, Li, Shengli, Wang, Weijun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Netherlands Elsevier B.V 10.05.2020
Témata:	algorithms business enterprises carbon markets Carbon price forecasting China Improved whale optimization algorithm industry Long short-term memory Modified ensemble empirical mode decomposition neural networks prediction time series analysis China Carbon price forecasting Improved whale optimization algorithm Long short-term memory Modified ensemble empirical mode decomposition
ISSN:	0048-9697, 1879-1026, 1879-1026
On-line přístup:	Získat plný text
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