Deep multi-hybrid forecasting system with random EWT extraction and variational learning rate algorithm for crude oil futures
作者：
Highlights：
• Multi-hybrid neural network with EWT extraction and q-DSCID evaluation is pro-posed.
• Novel algorithm based on random inheritance formula is advanced and engrafted.
• Ameliorated DBLSTMNN, integrates deep bidirectional training configuration.
• Variational learning rate arithmetic is put forward to optimize parameter selection.
• Results show the high accuracy of the proposed model for crude oil futures.
摘要
•Multi-hybrid neural network with EWT extraction and q-DSCID evaluation is pro-posed.•Novel algorithm based on random inheritance formula is advanced and engrafted.•Ameliorated DBLSTMNN, integrates deep bidirectional training configuration.•Variational learning rate arithmetic is put forward to optimize parameter selection.•Results show the high accuracy of the proposed model for crude oil futures.
论文关键词：Machine learning,Long short-term memory,Empirical wavelet transform,Deep bidirectional training structure,Random inheritance formula,Variational learning rate
论文评审过程：Received 5 December 2019, Revised 9 June 2020, Accepted 21 June 2020, Available online 2 July 2020, Version of Record 8 July 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113686
