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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Bibliographic Details
Published in:The Science of the total environment Vol. 716; p. 137117
Main Authors: Yang, Shaomei, Chen, Dongjiu, Li, Shengli, Wang, Weijun
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 10.05.2020
Subjects:
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
Online Access:Get full text
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Summary: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 optimization algorithm (IWOA). This model is based on the nonlinear and non-stationary characteristics of carbon price. The original carbon price is first decomposed into nine intrinsic mode functions (IMFs) and a residual using the MEEMD model. Then, the random forest method is applied to determine the input variables of each IMF and the residual, in the LSTM neural network. The carbon price is then predicted by the LSTM model optimized by the IWOA. The proposed hybrid model is applied to predict the carbon prices of Beijing, Fujian, and Shanghai to assess its effectiveness. The results reveal that the model achieved higher prediction performance than 11 other benchmark models. Our observations indicate that decomposition of carbon price can effectively improve the accuracy of prediction. Moreover, the improved LSTM model is more suitable for time series prediction. The proposed model provides a novel and effective carbon price forecasting tool for governments and enterprises. [Display omitted] •This study proposed a new hybrid carbon price forecasting model, MEEMD–IWOA–LSTM.•The efficacy of the proposed model was evaluated in different carbon trading markets.•The proposed model outperforms 11 other models in terms of carbon price prediction.•Decomposition by MEEMD could aid in forecasting carbon prices more accurately.•The improved LSTM model is suitable for time series forecasting.
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ISSN:0048-9697
1879-1026
1879-1026
DOI:10.1016/j.scitotenv.2020.137117