Fast detection of nonlinearity and nonstationarity in short and noisy time series
Laboratorio sui Sistemi Complessi, Scuola Superiore di Catania - Via San Nullo 5/i, 95123 Catania, Italy, EU and Dipartimento di Fisica e Astronomia, Università di Catania, and INFN - Via S. Sofia 64, 95123 Catania, Italy, EU
Accepted: 31 July 2010
We introduce a statistical method to detect nonlinearity and nonstationarity in time series, that works even for short sequences and in the presence of noise. The method has a discrimination power similar to that of the most advanced estimators on the market, yet it depends only on one parameter, is easier to implement and faster. Applications to real data sets reject the null hypothesis of an underlying stationary linear stochastic process with a higher confidence interval than the best known nonlinear discriminators up to date.
PACS: 05.45.Tp – Time series analysis / 05.10.-a – Computational methods in statistical physics and nonlinear dynamics
© EPLA, 2010