Detection of nonlinearity and chaoticity in time series using the transportation distance function

Sukanta Basu, Efi Foufoula-Georgiou

Research output: Contribution to journalArticle

21 Scopus citations

Abstract

We propose a systematic two-step framework to assess the presence of nonlinearity and chaoticity in time series. Although the basic components of this framework are from the well-known paradigm of surrogate data and the concept of short-term predictability, the newly proposed discriminating statistic, the transportation distance function offers several advantages (e.g., robustness against noise and outliers, fewer data requirements) over traditional measures of nonlinearity. The power of this framework is tested on several numerically generated series and the Santa Fe Institute competition series.

Original languageEnglish (US)
Pages (from-to)413-423
Number of pages11
JournalPhysics Letters, Section A: General, Atomic and Solid State Physics
Volume301
Issue number5-6
DOIs
StatePublished - Sep 2 2002

Keywords

  • Chaos
  • Nonlinearity
  • Short-term prediction
  • Surrogate data
  • Time series
  • Transportation distance

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