Deep Learning Abilities To Classify Intricate Variations In Temporal Dynamics Of Multivariate Time Series

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
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