  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.
