Time series classification via topological data analysis

In this paper, we develop topological data analysis methods for classification tasks on univariate time series. As an application, we perform binary and ternary classification tasks on two public datasets that consist of physiological signals collected under stress and non-stress conditions. We acco...

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Veröffentlicht in:Expert systems with applications Jg. 183; S. 115326
Hauptverfasser: Karan, Alperen, Kaygun, Atabey
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 30.11.2021
Elsevier BV
Schlagworte:
Classification
Data analysis
Homology
Machine learning
Persistent homology
Stress recognition
Time delay embedding
Time lag
Time series
Topology
Persistent homology
Time delay embedding
Machine learning
Stress recognition
ISSN:0957-4174, 1873-6793
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Zusammenfassung:In this paper, we develop topological data analysis methods for classification tasks on univariate time series. As an application, we perform binary and ternary classification tasks on two public datasets that consist of physiological signals collected under stress and non-stress conditions. We accomplish our goal by using persistent homology to engineer stable topological features after we use a time delay embedding of the signals and perform a subwindowing instead of using windows of fixed length. The combination of methods we use can be applied to any univariate time series and allows us to reduce noise and use long window sizes without incurring an extra computational cost. We then use machine learning models on the features we algorithmically engineered to obtain higher accuracies with fewer features.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2021.115326