Quasi Zigzag Persistence: A Topological Framework for Analyzing Time-Varying Data

Tamal K. Dey, Shreyas N. Samaga
2/22/2025
cs.LGmath.AT

No official code found. Best implementation: TDA-Jyamiti/zzgril (4 stars, last commit 2025-03-10).

Abstract

In this paper, we propose Quasi Zigzag Persistent Homology (QZPH) as a framework for analyzing time-varying data by integrating multiparameter persistence and zigzag persistence. To this end, we introduce a stable topological invariant that captures both static and dynamic features at different scales. We present an algorithm to compute this invariant efficiently. We show that it enhances the machine learning models when applied to tasks such as sleep-stage detection, demonstrating its effectiveness in capturing the evolving patterns in time-varying datasets.

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Code Implementations(1)

TDA-Jyamiti/zzgrilCommunity100%
40Shell, PythonFeb 14, 20251 years ago

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Cite this paper

@article{dey2025quasi,
  title  = {Quasi Zigzag Persistence: A Topological Framework for Analyzing Time-Varying Data},
  author = {Tamal K. Dey and Shreyas N. Samaga},
  year   = {2025},
  eprint = {2502.16049},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.16049v2}
}

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