Forecasting wavelet neural hybrid network with financial ensemble empirical mode decomposition and MCID evaluation
作者：
Highlights：
• Novel hybrid neural network is modeled with ensemble empirical mode decomposition.
• Random time effective function is applied to improve forecasting accuracy.
• Forecasting capacity of the hybrid model is compared with other models.
• Empirical results display superiority forecasting capacity of proposed model.
• Multiscale complexity invariant distance is applied in error evaluation.
摘要
•Novel hybrid neural network is modeled with ensemble empirical mode decomposition.•Random time effective function is applied to improve forecasting accuracy.•Forecasting capacity of the hybrid model is compared with other models.•Empirical results display superiority forecasting capacity of proposed model.•Multiscale complexity invariant distance is applied in error evaluation.
论文关键词：hybrid neural network prediction model,Energy market,Ensemble empirical mode decomposition,Multiscale complexity invariant distance
论文评审过程：Received 22 April 2019, Revised 29 August 2020, Accepted 3 October 2020, Available online 6 October 2020, Version of Record 7 October 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.114097
