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

Abstract : This work addresses the problem of analyzing multi-channel time series data by proposing an unsupervised fusion framework based on 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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https://hal.archives-ouvertes.fr/hal-02943658
Contributor : Emilie Chouzenoux <>
Submitted on : Sunday, September 20, 2020 - 11:46:25 AM
Last modification on : Saturday, October 10, 2020 - 3:25:52 AM
Long-term archiving on: : Thursday, December 3, 2020 - 2:20:21 PM

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  • HAL Id : hal-02943658, version 1

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Pooja Gupta, Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia. ConFuse: Convolutional Transform Learning Fusion Framework For Multi-Channel Data Analysis. 28th European Signal Processing Conference (EUSIPCO 2020), Jan 2021, Amsterdam, Netherlands. ⟨hal-02943658⟩

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