Interpretable time series neural representation for classification
  purposes
Etienne Le Naour Ghislain Agoua Nicolas Baskiotis Vincent Guigue
Deep learning has made significant advances in creating efficient
representations of time series data by automatically identifying complex
patterns. However, these approaches lack interpretability, as the time series
is transformed into a latent vector that is not easily interpretable. On the
other hand, Symbolic Aggregate approximation (SAX) methods allow the creation
of symbolic representations that can be interpreted but do not capture complex
patterns effectively. In this work, we propose a set of requirements for a
neural representation of univariate time series to be interpretable. We propose
a new unsupervised neural architecture that meets these requirements. The
proposed model produces consistent, discrete, interpretable, and visualizable
representations. The model is learned independently of any downstream tasks in
an unsupervised setting to ensure robustness. As a demonstration of the
effectiveness of the proposed model, we propose experiments on classification
tasks using UCR archive datasets. The obtained results are extensively compared
to other interpretable models and state-of-the-art neural representation
learning models. The experiments show that the proposed model yields, on
average better results than other interpretable approaches on multiple
datasets. We also present qualitative experiments to asses the interpretability
of the approach.