A Shift Test for Independence in Generic Time Series
Abstract
We describe a family of conservative statistical tests for independence of two autocorrelated time series. The series may take values in any sets, and one of them must be stationary. A user-specified function quantifying the association of a segment of the two series is compared to an ensemble obtained by time-shifting the stationary series -N to N steps. If the series are independent, the unshifted value is in the top m shifted values with probability at most m/(N+1). For large N, the probability approaches m/(2N+1). A conservative test rejects independence at significance {\alpha} if the unshifted value is in the top {\alpha}(N+1), and has half the power of an approximate test valid in the large N limit. We illustrate this framework with a test for correlation of autocorrelated categorical time series.
Cite
@article{arxiv.2012.06862,
title = {A Shift Test for Independence in Generic Time Series},
author = {Kenneth D. Harris},
journal= {arXiv preprint arXiv:2012.06862},
year = {2020}
}