Deep Learning Abilities to Classify Intricate Variations in Temporal Dynamics of Multivariate Time Series

Abstract

The aim of this work is to investigate the ability of deep learning (DL) architectures to learn temporal dynamics in multivariate time series. The methodology consists in using well known synthetic stochastic processes for which changes in joint temporal dynamics can be controlled. This permits to compare deep learning against classical machine learning techniques relying on documented hand-crafted wavelet-based features. First, we assess the performance of several different DL architectures and show the relevance of convolutional neural networks (CNN). Second, we test the robustness of CNN performance in classifying subtle changes in multivariate temporal dynamics with respect to learning conditions (dataset size, time series sample size, transfer learning).