A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting

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A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting. / Zhu, Bangzhu; Ye, Shunxin; Wang, Ping; He, Kaijian; Zhang, Tao; Wei, Yi-Ming.

In: Energy Economics, Vol. 70, 02.2018, p. 143-157.

Research output: Contribution to journalArticlepeer-review

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Zhu, Bangzhu ; Ye, Shunxin ; Wang, Ping ; He, Kaijian ; Zhang, Tao ; Wei, Yi-Ming. / A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting. In: Energy Economics. 2018 ; Vol. 70. pp. 143-157.

Bibtex

@article{29372afbb1b440a992a4e54bd66b82b9,
title = "A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting",
abstract = "In this study, a novel multiscale nonlinear ensemble leaning paradigm incorporating empirical mode decomposition (EMD) and least square support vector machine (LSSVM) with kernel function prototype is proposed for carbon price forecasting. The EMD algorithm is used to decompose the carbon price into simple intrinsic mode functions (IMFs) and one residue, which are identified as the components of high frequency, low frequency and trend by using the Lempel-Ziv complexity algorithm. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to forecast the high frequency IMFs with ARCH effects. The LSSVM model with kernel function prototype is employed to forecast the high frequency IMFs without ARCH effects, the low frequency and trend components. The forecasting values of all the components are aggregated into the ones of original carbon price by the LSSVM with kernel function prototype-based nonlinear ensemble approach. Furthermore, particle swarm optimization is used for model selections of the LSSVM with kernel function prototype. Taking the popular prediction methods as benchmarks, the empirical analysis demonstrates that the proposed model can achieve higher level and directional predictions and higher robustness. The findings show that the proposed model seems an advanced approach for predicting the high nonstationary, nonlinear and irregular carbon price.",
keywords = "Carbon price forecasting, Least squares support vector machine, Empirical mode decomposition, Particle swarm optimization, Kernel function prototype",
author = "Bangzhu Zhu and Shunxin Ye and Ping Wang and Kaijian He and Tao Zhang and Yi-Ming Wei",
year = "2018",
month = feb,
doi = "10.1016/j.eneco.2017.12.030",
language = "English",
volume = "70",
pages = "143--157",
journal = "Energy Economics",
issn = "0140-9883",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting

AU - Zhu, Bangzhu

AU - Ye, Shunxin

AU - Wang, Ping

AU - He, Kaijian

AU - Zhang, Tao

AU - Wei, Yi-Ming

PY - 2018/2

Y1 - 2018/2

N2 - In this study, a novel multiscale nonlinear ensemble leaning paradigm incorporating empirical mode decomposition (EMD) and least square support vector machine (LSSVM) with kernel function prototype is proposed for carbon price forecasting. The EMD algorithm is used to decompose the carbon price into simple intrinsic mode functions (IMFs) and one residue, which are identified as the components of high frequency, low frequency and trend by using the Lempel-Ziv complexity algorithm. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to forecast the high frequency IMFs with ARCH effects. The LSSVM model with kernel function prototype is employed to forecast the high frequency IMFs without ARCH effects, the low frequency and trend components. The forecasting values of all the components are aggregated into the ones of original carbon price by the LSSVM with kernel function prototype-based nonlinear ensemble approach. Furthermore, particle swarm optimization is used for model selections of the LSSVM with kernel function prototype. Taking the popular prediction methods as benchmarks, the empirical analysis demonstrates that the proposed model can achieve higher level and directional predictions and higher robustness. The findings show that the proposed model seems an advanced approach for predicting the high nonstationary, nonlinear and irregular carbon price.

AB - In this study, a novel multiscale nonlinear ensemble leaning paradigm incorporating empirical mode decomposition (EMD) and least square support vector machine (LSSVM) with kernel function prototype is proposed for carbon price forecasting. The EMD algorithm is used to decompose the carbon price into simple intrinsic mode functions (IMFs) and one residue, which are identified as the components of high frequency, low frequency and trend by using the Lempel-Ziv complexity algorithm. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to forecast the high frequency IMFs with ARCH effects. The LSSVM model with kernel function prototype is employed to forecast the high frequency IMFs without ARCH effects, the low frequency and trend components. The forecasting values of all the components are aggregated into the ones of original carbon price by the LSSVM with kernel function prototype-based nonlinear ensemble approach. Furthermore, particle swarm optimization is used for model selections of the LSSVM with kernel function prototype. Taking the popular prediction methods as benchmarks, the empirical analysis demonstrates that the proposed model can achieve higher level and directional predictions and higher robustness. The findings show that the proposed model seems an advanced approach for predicting the high nonstationary, nonlinear and irregular carbon price.

KW - Carbon price forecasting

KW - Least squares support vector machine

KW - Empirical mode decomposition

KW - Particle swarm optimization

KW - Kernel function prototype

U2 - 10.1016/j.eneco.2017.12.030

DO - 10.1016/j.eneco.2017.12.030

M3 - Article

VL - 70

SP - 143

EP - 157

JO - Energy Economics

JF - Energy Economics

SN - 0140-9883

ER -