Accurate estimator of correlations between asynchronous signals

TL;DR

This paper introduces a novel method for accurately estimating correlations between asynchronous signals without using long time windows, improving precision and computational efficiency.

Contribution

The paper presents a new approach that decomposes long-window correlations using short-window lagged correlations, enhancing accuracy and reducing computational effort.

Findings

Significantly improved correlation estimation accuracy

Reduced computational effort in real and simulated data

Effective handling of asynchronous signals without long windows

Abstract

The estimation of the correlation between time series is often hampered by the asynchronicity of the signals. Cumulating data within a time window suppresses this source of noise but weakens the statistics. We present a method to estimate correlations without applying long time windows. We decompose the correlations of data cumulated over a long window using decay of lagged correlations as calculated from short window data. This increases the accuracy of the estimated correlation significantly and decreases the necessary efforts of calculations both in real and computer experiments.