Abstract
Pattern changing in time series refers to structural variations in time domain, which, in turn, represents transitions between different states. Since the same state (a piece of time series pattern) can be largely varied in detail, therefore, pattern changing detection in time series is still a hard problem. Topological data analysis (TDA) allows a characterization of time-series data obtained from complex dynamical systems. In this paper, we present a pattern changing detection technique based on TDA. Given a time series, the signal is divided in non-overlapped slicing windows. For each window, we calculate the persistent homology, i.e., the associated barcode. From the barcode, some measures, like the average interval size and persistent entropy, are extracted and plotted against the signal duration. The changing points can be revealed by the measures. Experimental results on artificial and real data sets show promising results of the proposed method.
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Miranda, V., Zhao, L. (2020). Topological Data Analysis for Time Series Changing Point Detection. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_21
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DOI: https://doi.org/10.1007/978-3-030-32591-6_21
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