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Estimation of connectivity measures in gappy time series

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  • Papadopoulos, G.
  • Kugiumtzis, D.

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.

Suggested Citation

  • Papadopoulos, G. & Kugiumtzis, D., 2015. "Estimation of connectivity measures in gappy time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 387-398.
    • Handle: RePEc:eee:phsmap:v:436:y:2015:i:c:p:387-398
    DOI: 10.1016/j.physa.2015.05.032
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    Cited by:

    1. Papana, Angeliki & Kyrtsou, Catherine & Kugiumtzis, Dimitris & Diks, Cees, 2017. "Financial networks based on Granger causality: A case study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 65-73.
    2. Elsegai, Heba, 2019. "Granger-causality inference in the presence of gaps: An equidistant missing-data problem for non-synchronous recorded time series data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 839-851.

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