SuperDeConFuse: A supervised deep convolutional transform based fusion framework for financial trading systems
作者：
Highlights：
• The work proposes a novel supervised learning framework, for fusion of multi-channel information.
• It is based on the recently established Convolutional Transform Learning technique.
• The practical goal is to perform classification tasks on stock trading financial data.
• The model keeps up with the univariate and sequential nature of the time-series financial data.
• It improves over the state-of-the-art methods.
摘要
•The work proposes a novel supervised learning framework, for fusion of multi-channel information.•It is based on the recently established Convolutional Transform Learning technique.•The practical goal is to perform classification tasks on stock trading financial data.•The model keeps up with the univariate and sequential nature of the time-series financial data.•It improves over the state-of-the-art methods.
论文关键词：Information fusion,Deep learning,Convolution,Transform learning,Stock trading
论文评审过程：Received 27 August 2020, Revised 28 October 2020, Accepted 31 October 2020, Available online 10 November 2020, Version of Record 10 February 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.114206
