Detecting nonlinearity in multivariate time series
Abstract
We propose an extension to time series with several simultaneously measured variables of the nonlinearity test, which combines the redundancy -- linear redundancy approach with the surrogate data technique. For several variables various types of the redundancies can be defined, in order to test specific dependence structures between/among (groups of) variables. The null hypothesis of a multivariate linear stochastic process is tested using the multivariate surrogate data. The linear redundancies are used in order to avoid spurious results due to imperfect surrogates. The method is demonstrated using two types of numerically generated multivariate series (linear and nonlinear) and experimental multivariate data from meteorology and physiology.
Keywords
Cite
@article{arxiv.comp-gas/9507004,
title = {Detecting nonlinearity in multivariate time series},
author = {Milan Paluš},
journal= {arXiv preprint arXiv:comp-gas/9507004},
year = {2009}
}
Comments
11 pages, compressed and uuencoded postscript file, figures included. Also available by anonymous ftp at ftp://ftp.santafe.edu/pub/mp/multi, E-mail: [email protected], [email protected]