Estimation of connectivity measures in gappy time series

TL;DR

This paper introduces MAGR, a novel method for estimating connectivity in gappy multivariate time series by removing rows with gaps, avoiding gap filling, and demonstrating superior performance over existing techniques.

Contribution

The paper presents MAGR, a new gap removal method applicable to various connectivity measures, improving estimation accuracy in incomplete time series data.

Findings

MAGR outperforms gap-filling techniques in synthetic and financial data.

MAGR maintains accuracy across different connectivity measures.

The method is versatile and applicable to multiple types of time series analysis.

Abstract

A new method is proposed to compute connectivity measures on multivariate time series with gaps. Rather than removing or filling the gaps, the rows of the joint data matrix containing empty entries are removed and the calculations are done on the remainder matrix. The method, called measure adapted gap removal (MAGR), can be applied to any connectivity measure that uses a joint data matrix, such as cross correlation, cross mutual information and transfer entropy. MAGR is favorably compared using these three measures to a number of known gap-filling techniques, as well as the gap closure. The superiority of MAGR is illustrated on time series from synthetic systems and financial time series.