A Wide-Sense Stationarity Test Based on the Geometric Structure of Covariance
By: Wang Yinbu, Xu Yong
This paper presents a test for wide-sense stationarity (WSS) based on the geometry of the covariance function. We estimate local patches of the covariance surface and then check whether the directional derivative in the $(1,1,0)$ direction is zero on each patch. The method only requires the covariance function to be locally smooth and does not assume stationarity in advance. It can be applied to general stochastic dynamical systems and provides a time-resolved view. We apply the test method to an SDOF system and to a stochastic Duffing oscillator. These examples show that the method is numerically stable and can detect departures from WSS in practice.
Similar Papers
Correlation tests and sample spectral coherence matrix in the high-dimensional regime
Statistics Theory
Tests if data streams are truly separate.
Notes on Correlation Stress Tests
Risk Management
Tests how money risks change together.
Testing for latent structure via the Wilcoxon--Wigner random matrix of normalized rank statistics
Methodology
Find hidden patterns in big data sets.