Testing for nonlinearity in unevenly sampled time series
chao-dyn
2009-10-31 v1 Chaotic Dynamics
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.
Cite
@article{arxiv.chao-dyn/9804042,
title = {Testing for nonlinearity in unevenly sampled time series},
author = {Andreas Schmitz and Thomas Schreiber},
journal= {arXiv preprint arXiv:chao-dyn/9804042},
year = {2009}
}
Comments
4 pages, 4 figues