Sliding Windows and Persistence: An Application of Topological Methods to Signal Analysis

Journal Article

© 2014, SFoCM.We develop in this paper a theoretical framework for the topological study of time series data. Broadly speaking, we describe geometrical and topological properties of sliding window embeddings, as seen through the lens of persistent homology. In particular, we show that maximum persistence at the point-cloud level can be used to quantify periodicity at the signal level, prove structural and convergence theorems for the resulting persistence diagrams, and derive estimates for their dependency on window size and embedding dimension. We apply this methodology to quantifying periodicity in synthetic data sets and compare the results with those obtained using state-of-the-art methods in gene expression analysis. We call this new method SW1PerS, which stands for Sliding Windows and 1-Dimensional Persistence Scoring.

Full Text

Duke Authors

Cited Authors

  • Perea, JA; Harer, J

Published Date

  • 2014

Published In

Volume / Issue

  • 15 / 3

Start / End Page

  • 799 - 838

International Standard Serial Number (ISSN)

  • 1615-3375

Digital Object Identifier (DOI)

  • 10.1007/s10208-014-9206-z