Time series classification via topological data analysis

Karan A. , Kaygun A.

EXPERT SYSTEMS WITH APPLICATIONS, vol.183, 2021 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 183
  • Publication Date: 2021
  • Doi Number: 10.1016/j.eswa.2021.115326
  • Keywords: Persistent homology, Time delay embedding, Machine learning, Stress recognition, PERSISTENT HOMOLOGY


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.