4.7 Article

Hodrick-Prescott filter-based hybrid ARIMA-SLFNs model with residual decomposition scheme for carbon price forecasting

Journal

APPLIED SOFT COMPUTING
Volume 119, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.asoc.2022.108560

Keywords

Carbon price forecasting; Hybrid framework; Hodrick-Prescott filter; Residual decomposition; Kernel extreme learning machine

Funding

  1. National Natural Science Foundation of China [71871146, 72074028, 72174124]
  2. Guangdong Special Support Program for Young Top-notch Talent in Science and Technology Innovation, China [2019TQ05L989]
  3. Natural Science Foundation of Guangdong Province, China [2021A1515011777]
  4. Research Platforms and Project in Ordinary Universities of Education Department of Guangdong Province, China [2020WTSCX079]
  5. Planning Project of Philosophy and Social Science in Shenzhen, China [SZ2020D017]
  6. MOE (Ministry of Education in China) Project of Humanities and Social Science, China [18YJA630090]
  7. Beijing Institute of Technology Research Fund Program for Young Scholars, China [2020CX04206]
  8. Science and Technology Innovation Plan of Beijing Institute of Technology [2021CX02051]
  9. Guangdong Science and Technology Innovation Strategy (Climbing Plan) Special Project [pdjh2021b0437]

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Accurate carbon pricing guidance is crucial for reducing carbon dioxide emissions. This study proposes a novel filter-based model using the Hodrick-Prescott filter for carbon price forecasting. The model incorporates adaptive noise residual decomposition and Bayesian optimization to improve performance. Compared to existing models, it shows better stability and statistical advantage.
Accurate carbon pricing guidance is of great importance for the inhibition of excessive carbon dioxide emissions. Aiming at improving forecast performance, a number of carbon price forecasting models have been proposed based on the combination or multiscale hybrid frameworks. However, most of these hybrid models cannot easily cast a perfect reflection of erratic fluctuation in carbon trading schemes due to lack of judgment on the trend or inaccurate trend reconstruction. In this study, a novel filter-based modeling with Hodrick-Prescott (HP) filter, that can identify repeated up and down structural features around a certain carbon price, negotiates the obstacle of the parallel-series hybridization concerning the linear and the nonlinear model identification. The residual decomposition scheme with adaptive noise is carried out on the random and nonlinear component for error correction to filter-based models. Moreover, Bayesian optimization adjusts the structure of seven single-hidden layer feedforward neural networks (SLFNs) and the inputs to provide the best generalization performance. The proposed filter-hybrid model using kernel extreme learning machine as the final nonlinear integrator has better stability to the parameters, and has the superiority over the parallel-series and allocation-based models from a statistical perspective. Comparing with existing data-driven models, our proposed model is competitive in view of prediction accuracy and time cost in the majority of carbon futures trading cases.(C) 2022 Elsevier B.V. All rights reserved.

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