Permutation Learning in Convolutional Neural Networks for Time-Series Analysis

This study proposes a novel module in the convolutional neural networks (CNN) framework named permutation layer. With the new layer, we are particularly targeting time-series tasks where 2-dimensional CNN kernel loses its ability to capture the spatially co-related features. Multivariate time-series analysis consists of stacked input channels without considering the order of the channels resulting in an unsorted "2D-image". 2D convolution kernels are not efficient at capturing features from these distorted as the time-series lacks spatial information between the sensor channels. To overcome this weakness, we propose learnable permutation layers as an extension of vanilla convolution layers which allow to interchange different sensor channels such that sensor channels with similar information content are brought together to enable a more effective 2D convolution operation. We test the approach on a benchmark time-series classification task and report the superior performance and applicability of the proposed method.