Preservation of long range temporal correlations under extreme random dilution

Dror Mirzayof, Yosef Ashkenazy

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Many natural time series exhibit long range temporal correlations that may be characterized by power-law scaling exponents. However, in many cases, the time series have uneven time intervals due to, for example, missing data points, noisy data, and outliers. Here we study the effect of randomly missing data points on the power-law scaling exponents of time series that are long range temporally correlated. The Fourier transform and detrended fluctuation analysis (DFA) techniques are used for scaling exponent estimation. We find that even under extreme dilution of more than 50%, the value of the scaling exponent remains almost unaffected. Random dilution is also applied on heart interbeat interval time series. It is found that dilution of 70%80% of the data points leads to a reduction of only 8% in the scaling exponent; it is also found that it is possible to discriminate between healthy and heart failure subjects even under extreme dilution of more than 90%.

Original languageEnglish
Pages (from-to)5573-5580
Number of pages8
JournalPhysica A: Statistical Mechanics and its Applications
Issue number24
StatePublished - 15 Dec 2010


  • DFA
  • Heart interbeat interval
  • Long range correlations
  • Random dilution
  • Time series

ASJC Scopus subject areas

  • Statistics and Probability
  • Condensed Matter Physics


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