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• W2951543185 abstract "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 (or time-delay) 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." @default.
• W2951543185 created "2019-06-27" @default.
• W2951543185 creator A5017700107 @default.
• W2951543185 creator A5046061686 @default.
• W2951543185 date "2013-07-23" @default.
• W2951543185 modified "2023-09-27" @default.
• W2951543185 title "Sliding Windows and Persistence: An Application of Topological Methods to Signal Analysis" @default.
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