Wavelet space partitioning for symbolic time series analysis

Venkatesh Rajagopalan, Asok Ray

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

A crucial step in symbolic time series analysis (STSA) of observed data is symbol sequence generation that relies on partitioning the phase-space of the underlying dynamical system. We present a novel partitioning method, called wavelet-space (WS) partitioning, as an alternative to symbolic false nearest neighbour (SFNN) partitioning. While the WS and SFNN partitioning methods have been demonstrated to yield comparable performance for anomaly detection on laboratory apparatuses, computation of WS partitioning is several orders of magnitude faster than that of the SFNN partitioning.

Original languageEnglish (US)
Article number081
Pages (from-to)1951-1954
Number of pages4
JournalChinese Physics Letters
Volume23
Issue number7
DOIs
StatePublished - Jul 1 2006

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

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