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

Pooja Gupta (1) , Jyoti Maggu (1) , Angshul Majumdar (1) , Emilie Chouzenoux (2) , Giovanni Chierchia (3, 4)

1 IIIT-Delhi - Indraprastha Institute of Information Technology [New Delhi]
2 OPIS - OPtimisation Imagerie et Santé
3 ESIEE Paris
4 LIGM - Laboratoire d'Informatique Gaspard-Monge

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.

Mots clés: CNN; transform learning; information fusion; stock forecasting and trading; finance data processing

Domaines: Traitement du signal et de l'image [eess.SP]; Intelligence artificielle [cs.AI]

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