# A Shift Test for Independence in Generic Time Series

arXiv: https://arxiv.org/abs/2012.06862 · HTML: https://mlsanity.com/paper/2012.06862/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2020.

## Built on by (most cited first, 1 of 1)

- [Feedback-Robust AI for Patient Knowledge Graphs](https://mlsanity.com/paper/2609.33248/) (2026)

## Abstract (arXiv)

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 α if the unshifted value is in the top α(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.