ConFuse: Convolutional Transform Learning Fusion Framework For Multi-Channel Data Analysis
This work addresses stock forecasting and trading using multi-channel financial data, but it appears incremental as it builds on existing convolutional transform learning methods.
The paper tackles the problem of analyzing multi-channel time series data by proposing an unsupervised fusion framework based on convolutional transform learning, applied to financial data for stock forecasting and trading, and reports that it yields considerably better results than benchmark deep time series analysis networks.
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.