Pattern identification in dynamical systems via symbolic time series analysis

Venkatesh Rajagopalan, Asok Ray, Rohan Samsi, Jeffrey Mayer

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

This paper presents symbolic time series analysis (STSA) of multi-dimensional measurement data for pattern identification in dynamical systems. The proposed methodology is built upon concepts derived from Information Theory and Automata Theory. The objective is not merely to classify the time series patterns but also to identify the variations therein. To achieve this goal, a symbol alphabet is constructed from raw data through partitioning of the data space. The maximum entropy method of partitioning is extended to multi-dimensional space. The resulting symbol sequences, generated from time series data, are used to model the dynamical information as finite state automata and the patterns are represented by the stationary state probability distributions. A novel procedure for determining the structure of the finite state automata, based on entropy rate, is introduced. The diversity among the observed patterns is quantified by a suitable measure. The efficacy of the STSA technique for pattern identification is demonstrated via laboratory experimentation on nonlinear systems.

Original languageEnglish (US)
Pages (from-to)2897-2907
Number of pages11
JournalPattern Recognition
Volume40
Issue number11
DOIs
StatePublished - Nov 2007

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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