An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting |
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| Authors: | Bangzhu Zhu Xuetao Shi Julien Chevallier Ping Wang Yi‐Ming Wei |
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| Affiliation: | 1. School of Management, Jinan University, Guangzhou, Guangdong, China;2. School of Economics and Management, Wuyi University, Jiangmen, Guangdong, China;3. IPAG Business School, IPAG Lab and Université Paris 8, LED, Paris, France;4. Center for Energy and Environmental Policy Research, Beijing Institute of Technology, China;5. School of Management and Econmics, Beijing Institute of Technology, China |
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| Abstract: | For forecasting nonstationary and nonlinear energy prices time series, a novel adaptive multiscale ensemble learning paradigm incorporating ensemble empirical mode decomposition (EEMD), particle swarm optimization (PSO) and least square support vector machines (LSSVM) with kernel function prototype is developed. Firstly, the extrema symmetry expansion EEMD, which can effectively restrain the mode mixing and end effects, is used to decompose the energy price into simple modes. Secondly, by using the fine‐to‐coarse reconstruction algorithm, the high‐frequency, low‐frequency and trend components are identified. Furthermore, autoregressive integrated moving average is applicable to predicting the high‐frequency components. LSSVM is suitable for forecasting the low‐frequency and trend components. At the same time, a universal kernel function prototype is introduced for making up the drawbacks of single kernel function, which can adaptively select the optimal kernel function type and model parameters according to the specific data using the PSO algorithm. Finally, the prediction results of all the components are aggregated into the forecasting values of energy price time series. The empirical results show that, compared with the popular prediction methods, the proposed method can significantly improve the prediction accuracy of energy prices, with high accuracy both in the level and directional predictions. Copyright © 2016 John Wiley & Sons, Ltd. |
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| Keywords: | nonstationary and nonlinear time series forecasting energy price prediction multiscale ensemble learning paradigm ensemble empirical mode decomposition least square support vector machines |
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