TOTOPO: Classifying univariate and multivariate time series with Topological Data Analysis

Pilyugina Polina, Rodrigo Rivera-Castro, Evgeny Burnaev

Keywords: topological data analysis, time series classification

Abstract: This work is devoted to a comprehensive analysis of topological data analysis for time series classification. Previous works have significant shortcomings, such as lack of large-scale benchmarking or missing state-of-the-art methods. In this work, we propose TOTOPO for extracting topological descriptors from different types of persistence diagrams. The results suggest that TOTOPO significantly outperforms existing baselines in terms of accuracy. TOTOPO is also competitive with the state-of-the-art, being the best on 20\% of univariate and 40\% of multivariate time series datasets. This work validates the hypothesis that TDA-based approaches are robust to small perturbations in data and are useful for cases where periodicity and shape help discriminate between classes.

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