A Novel Hybrid Model for Forecasting China Carbon Price Using CEEMDAN and Extreme Learning Machine Optimized by Whale Algorithm

Li, Ni and Venus Liew, Khim Sen (2023) A Novel Hybrid Model for Forecasting China Carbon Price Using CEEMDAN and Extreme Learning Machine Optimized by Whale Algorithm. In: Applied Mathematics, Modeling and Computer Simulation. Advances in Transdisciplinary Engineering, 42 . IOS Press, pp. 657-666. ISBN 978-1-64368-458-1 (print) | 978-1-64368-459-8 (online)

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Abstract

The carbon market can provide economic incentives for manufacturing industry to reduce carbon emissions. This paper follows the idea of "primary decomposition- noise reduction-secondary decomposition- forecasting and integration", the contribution is constructing a hybrid carbon price forecasting model using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Extreme Learning Machine (ELM) optimized by the Whale Optimization Algorithm (WOA). The results conclude that, the CEEMDAN-type secondary decomposition hybrid models have high forecasting accuracy, the WOAELM-type models can effectively reduce the forecasting errors. Noteworthy, the forecasting errors RMSE, MAE and MAPE of the proposed CEEMDAN-SE-CEEMD-WOAELM model are 2.587, 2.04 and 0.108 respectively, that is the lowest in all the comparative models. The forecasting accuracy and reliability of the proposed model have been convinced. Those findings can provide valuable reference for manufacturing industry to reduce pollutant emissions and take low-carbon investment.

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