Estimation of connectivity measures in gappy time series

Georgios Papadopoulos and D. Kugiumtzis

Physica A: Statistical Mechanics and its Applications, 2015, vol. 436, issue C, 387-398

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.

Keywords: Multivariate time series analysis; Connectivity measures; Gaps in time series; Transfer entropy (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:436:y:2015:i:c:p:387-398

DOI: 10.1016/j.physa.2015.05.032

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