Multi-WRNN model for pricing the crude oil futures market

作者：

Highlights：

• Proposing a multi-factor wavelet-based recurrent neural network model for crude oil futures pricing.

• Using multiresolution decomposition in the model to predict non-stationary time series.

• Introducing a flexible and versatile model to include new key factors.

• Analyzing several local wavelets for constructing the wavelet-based model for the pricing.

摘要

•Proposing a multi-factor wavelet-based recurrent neural network model for crude oil futures pricing.•Using multiresolution decomposition in the model to predict non-stationary time series.•Introducing a flexible and versatile model to include new key factors.•Analyzing several local wavelets for constructing the wavelet-based model for the pricing.

论文关键词：Discrete wavelet transform (DWT),B-spline multiresolution,Deep recurrent neural network (DRNN),Pricing future market,Time series forecasting,Volatility predicting

论文评审过程：Received 1 October 2020, Revised 11 February 2021, Accepted 16 May 2021, Available online 27 May 2021, Version of Record 2 June 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115229