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Method to Discriminate Against Determinism in Time Series Data

  • Robert Cawley
  • Guan-Hsong Hsu
  • Liming W. Salvino
Part of the Springer Series in Synergetics book series (SSSYN, volume 69)

Abstract

We describe a general, systematic method for assessing the presence or absence of determinism in time series. Our method is rooted in the standard engineering paradigm of hypothesis testing. Our application of this procedure is novel, however, for we test given data sets against the class of data sets that produce smoothness. That is, our null hypothesis is that of determinism. We highlight two inherently interactive key features of our approach which conspire to make this treatment promising, the use of a smoothness detector and of chaotic noise reduction.

Keywords

Time Series Vector Field Fractal Dimension Phase Portrait Noise Reduction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Robert Cawley
  • Guan-Hsong Hsu
  • Liming W. Salvino

There are no affiliations available

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