ConFuse: Convolutional Transform Learning Fusion Framework For Multi-Channel Data Analysis

• URL: http://arxiv.org/abs/2011.04317v1
• Date: Mon, 9 Nov 2020 10:41:28 GMT
• Title: ConFuse: Convolutional Transform Learning Fusion Framework For Multi-Channel Data Analysis
• Authors: Pooja Gupta, Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia
• Abstract summary: We propose an unsupervised fusion framework based on %the recently proposed convolutional transform learning. We apply the framework to multi-channel financial data for stock forecasting and trading.
• Score: 29.58965424136611
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work addresses the problem of analyzing multi-channel time series data %. In this paper, we by proposing an unsupervised fusion framework based on %the recently proposed convolutional transform learning. Each channel is processed by a separate 1D convolutional transform; the output of all the channels are fused by a fully connected layer of transform learning. The training procedure takes advantage of the proximal interpretation of activation functions. We apply the developed framework to multi-channel financial data for stock forecasting and trading. We compare our proposed formulation with benchmark deep time series analysis networks. The results show that our method yields considerably better results than those compared against.

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