Making Time Series Embeddings More Interpretable in Deep Learning: Extracting Higher-Level Features via Symbolic Approximation Representations

Abstract

With the success of language models in deep learning, mul-tiple new time series embeddings have been proposed. How-ever, the interpretability of those representations is often still lacking compared to word embeddings. This paper tackles this issue, aiming to present some criteria for making time series embeddings applied in deep learning models more in-terpretable using higher-level features in symbolic form. For that, we investigate two different approaches for extracting symbolic approximation representations regarding the fre-quency and the trend information, i. e., the Symbolic Fourier Approximation (SFA) and the Symbolic Aggregate approxi-mation (SAX). In particular, we analyze and discuss the im-pact of applying the different representation approaches. Fur-thermore, in our experimentation, we apply a state-of-the-art Transformer model to demonstrate the efficacy of the pro-posed approach regarding explainability in a comprehensive evaluation using a large set of time series datasets. © 2023 by the authors. All rights reserved.