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

Sukanta Basu, Efi Foufoula-Georgiou

Research output: Contribution to journalArticlepeer-review

22 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

Bibliographical note

Funding Information:
This work was partially supported by NSF (grant ATM-013094) and NASA (grants NAG5-7715 and NAG8-1519) and the computational resources were provided by the Minnesota Supercomputing Institute. All these sponsors are gratefully acknowledged. The authors want to thank Richard Moeckel, Joydeep Bhattacharya, Thomas Schreiber, and Rainer Hegger for useful guidance and discussion during the course of this work.

Keywords

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

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