TY - JOUR
T1 - An effective and robust decomposition-ensemble energy price forecasting paradigm with local linear prediction
AU - Qin, Quande
AU - Xie, Kangqiang
AU - He, Huangda
AU - Li, Li
AU - Chu, Xianghua
AU - Wei, Yi Ming
AU - Wu, Teresa
N1 - Funding Information:
The authors thank anonymous referees and an editor of this journal for their valuable comments. This work is partly supported by National Natural Science Foundation of China (Nos. 71871146 and 71402103 ), the Major Research Plan of the National Natural Science Foundation of China (No. 91846301 ), and the MOE (Ministry of Education in China) project of Humanities and Social Science at universities (NO. 18YJA630090 ).
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/9
Y1 - 2019/9
N2 - Energy price time series exhibit nonlinear and nonstationary features, which make accurate forecasting energy prices challenging. In this paper, we propose a novel decomposition-ensemble forecasting paradigm based on ensemble empirical mode decomposition (EEMD) and local linear prediction (LLP). The EEMD is used to decompose energy price time series into components, including several intrinsic mode functions and one residual with a simplified structure. Motivated by the findings of the fully local characteristics of a time series decomposed by the EEMD, we adopt the LLP technique to forecast each component. The forecasting results of all the components are aggregated as a final forecast. For validation, three types of energy price time series, crude oil, electricity and natural gas prices, are studied. The experimental results indicate that the proposed model achieves an improvement in terms of both level forecasting and direction forecasting. The performance of the proposed model is also validated through comparison with several energy price forecasting approaches from the literature. In addition, the robustness and the effects of the parameter settings of LLP are investigated. We conclude the proposed model is easy to implement and efficient for energy price forecasting.
AB - Energy price time series exhibit nonlinear and nonstationary features, which make accurate forecasting energy prices challenging. In this paper, we propose a novel decomposition-ensemble forecasting paradigm based on ensemble empirical mode decomposition (EEMD) and local linear prediction (LLP). The EEMD is used to decompose energy price time series into components, including several intrinsic mode functions and one residual with a simplified structure. Motivated by the findings of the fully local characteristics of a time series decomposed by the EEMD, we adopt the LLP technique to forecast each component. The forecasting results of all the components are aggregated as a final forecast. For validation, three types of energy price time series, crude oil, electricity and natural gas prices, are studied. The experimental results indicate that the proposed model achieves an improvement in terms of both level forecasting and direction forecasting. The performance of the proposed model is also validated through comparison with several energy price forecasting approaches from the literature. In addition, the robustness and the effects of the parameter settings of LLP are investigated. We conclude the proposed model is easy to implement and efficient for energy price forecasting.
KW - Energy price
KW - Ensemble empirical mode decomposition
KW - Forecasting
KW - Local linear prediction
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U2 - 10.1016/j.eneco.2019.07.026
DO - 10.1016/j.eneco.2019.07.026
M3 - Article
AN - SCOPUS:85070490691
SN - 0140-9883
VL - 83
SP - 402
EP - 414
JO - Energy Economics
JF - Energy Economics
ER -