False positive control in time series coincidence detection

TL;DR

This paper develops rigorous finite-sample methods for controlling false positives in time series coincidence detection, ensuring reliable inference in applications like astrophysics and neuroscience.

Contribution

It provides the first finite-sample guarantees for false positive control in time-shifting coincidence detection methods under dependent data.

Findings

Finite-sample guarantees for false positive control

Effective time-shifting methods validated on real data

Robust performance with dependent time series data

Abstract

We study the problem of coincidence detection in time series data, where we aim to determine whether the appearance of simultaneous or near-simultaneous events in two time series is indicative of some shared underlying signal or synchronicity, or might simply be due to random chance. This problem arises across many applications, such as astrophysics (e.g., detecting astrophysical events such as gravitational waves, with two or more detectors) and neuroscience (e.g., detecting synchronous firing patterns between two or more neurons). In this work, we consider methods based on time-shifting, where the timeline of one data stream is randomly shifted relative to another, to mimic the types of coincidences that could occur by random chance. Our theoretical results establish rigorous finite-sample guarantees controlling the probability of false positives, under weak assumptions that allow for dependence within the time series data, providing reassurance that time-shifting methods are a reliable tool for inference in this setting. Empirical results with simulated and real data validate the strong performance of time-shifting methods in dependent-data settings.