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Testing Time Series for Nonlinearity

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Abstract

The technique of surrogate data analysis may be employed to test the hypothesis that an observed data set was generated by one of several specific classes of dynamical system. Current algorithms for surrogate data analysis enable one, in a generic way, to test for membership of the following three classes of dynamical system: (0) independent and identically distributed noise, (1) linearly filtered noise, and (2) a monotonic nonlinear transformation of linearly filtered noise.

We show that one may apply statistics from nonlinear dynamical systems theory, in particular those derived from the correlation integral, as test statistics for the hypothesis that an observed time series is consistent with each of these three linear classes of dynamical system. Using statistics based on the correlation integral we show that it is also possible to test much broader (and not necessarily linear) hypotheses.

We illustrate these methods with radial basis models and an algorithm to estimate the correlation dimension. By exploiting some special properties of this correlation dimension estimation algorithm we are able to test very specific hypotheses. Using these techniques we demonstrate the respiratory control of human infants exhibits a quasi-periodic orbit (the obvious inspiratory/expiratory cycle) together with cyclic amplitude modulation. This cyclic amplitude modulation manifests as a stable focus in the first return map (equivalently, the sequence of successive peaks).

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Authors and Affiliations

  1. Centre for Applied Dynamics and Optimization, Department of Mathematics and Statistics, University of Western, Australia

    Michael Small, Kevin Judd & Alistair Mees

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  1. Michael Small
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  2. Kevin Judd
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  3. Alistair Mees
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Small, M., Judd, K. & Mees, A. Testing Time Series for Nonlinearity. Statistics and Computing 11, 257–268 (2001). https://doi.org/10.1023/A:1016604405201

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