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

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 for...

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Main Authors: Li, Ni, Venus Liew, Khim Sen
Other Authors: Chen, Chi Hua
Format: Book Chapter
Language:English
Published: IOS Press 2023
Subjects:
HG Finance
QA Mathematics
Online Access:http://ir.unimas.my/id/eprint/44125/2/A%20Novel%20Hybrid.pdf
http://ir.unimas.my/id/eprint/44125/
https://ebooks.iospress.nl/doi/10.3233/ATDE231006
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spelling my.unimas.ir.441252024-01-16T03:23:48Z http://ir.unimas.my/id/eprint/44125/ A Novel Hybrid Model for Forecasting China Carbon Price Using CEEMDAN and Extreme Learning Machine Optimized by Whale Algorithm Li, Ni Venus Liew, Khim Sen HG Finance QA Mathematics 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. IOS Press Chen, Chi Hua Andrea, Scapellato Alessandro, Barbiero Dmitry, G. Korzun 2023-12-15 Book Chapter PeerReviewed text en http://ir.unimas.my/id/eprint/44125/2/A%20Novel%20Hybrid.pdf 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) https://ebooks.iospress.nl/doi/10.3233/ATDE231006 doi:10.3233/ATDE231006
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic HG Finance
QA Mathematics
spellingShingle HG Finance
QA Mathematics
Li, Ni
Venus Liew, Khim Sen
A Novel Hybrid Model for Forecasting China Carbon Price Using CEEMDAN and Extreme Learning Machine Optimized by Whale Algorithm
description 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.
author2 Chen, Chi Hua
author_facet Chen, Chi Hua
Li, Ni
Venus Liew, Khim Sen
format Book Chapter
author Li, Ni
Venus Liew, Khim Sen
author_sort Li, Ni
title A Novel Hybrid Model for Forecasting China Carbon Price Using CEEMDAN and Extreme Learning Machine Optimized by Whale Algorithm
title_short A Novel Hybrid Model for Forecasting China Carbon Price Using CEEMDAN and Extreme Learning Machine Optimized by Whale Algorithm
title_full A Novel Hybrid Model for Forecasting China Carbon Price Using CEEMDAN and Extreme Learning Machine Optimized by Whale Algorithm
title_fullStr A Novel Hybrid Model for Forecasting China Carbon Price Using CEEMDAN and Extreme Learning Machine Optimized by Whale Algorithm
title_full_unstemmed A Novel Hybrid Model for Forecasting China Carbon Price Using CEEMDAN and Extreme Learning Machine Optimized by Whale Algorithm
title_sort novel hybrid model for forecasting china carbon price using ceemdan and extreme learning machine optimized by whale algorithm
publisher IOS Press
publishDate 2023
url http://ir.unimas.my/id/eprint/44125/2/A%20Novel%20Hybrid.pdf
http://ir.unimas.my/id/eprint/44125/
https://ebooks.iospress.nl/doi/10.3233/ATDE231006
_version_ 1789430364067856384
score 13.211869

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