Interactive Motif Discovery in Time Series with Persistent Homology

Thibaut Germain, Charles Truong, Laurent Oudre

Abstract: Time series analysis based on recurrent patterns, also called motifs, has emerged as a powerful approach in various domains. However, uncovering recurrent patterns poses challenges and usually requires expert knowledge. This paper introduces an interactive version of the PersistentPattern algorithm (PEPA), which addresses these challenges by leveraging topological data analysis. PEPA provides a visually intuitive representation of time series, facilitating motif selection without needing expert knowledge. Our work aims to empower data mining and machine learning researchers seeking deeper insights into time series. We provide an overview of the PEPA algorithm and detail its interactive version, concluding with a demonstration of abnormal heartbeat detection.