Testing for nonlinearity in unevenly sampled time series

Abstract

We generalize the method of surrogate data of testing for nonlinearity in time series to the case that the data are sampled with uneven time intervals. The null hypothesis will be that the data have been generated by a linear stochastic process, possibly rescaled, and sampled at times chosen independently from the generating process. The surrogate data are generated with their linear properties specified by the Lomb periodogram. The inversion problem is solved by combinatorial optimization.

4 pages, 4 figues

References in corpus (3)

• Improved surrogate data for nonlinearity tests
• Discrimination power of measures for nonlinearity in a time series
• Constrained randomization of time series data

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