Time-optimal persistent homology representatives for univariate time series

TL;DR

This paper introduces a new method for computing time-optimal persistent homology representatives for time-varying data, improving interpretability and applicability in fields like climate modeling.

Contribution

The paper proposes a novel concept of time-optimal PH representatives tailored for time series, addressing computational challenges and enhancing interpretability.

Findings

Time-optimal PH representatives better capture temporal features.

Method outperforms length-optimal representatives in synthetic and climate data.

Provides practical algorithms for time-varying data analysis.

Abstract

Persistent homology (PH) is one of the main methods used in Topological Data Analysis. An active area of research in the field is the study of appropriate notions of PH representatives, which allow to interpret the meaning of the information provided by PH, making it an important problem in the application of PH, and in the study of its interpretability. Computing optimal PH representatives is a problem that is known to be NP-hard, and one is therefore interested in developing context-specific optimality notions that are computable in practice. Here we introduce time-optimal PH representatives for time-varying data, allowing one to extract representatives that are close in time in an appropriate sense. We illustrate our methods on quasi-periodic synthetic time series, as well as time series arising from climate models, and we show that our methods provide optimal PH representatives that are better suited for these types of problems than existing optimality notions, such as length-optimal PH representatives.