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Long-memory analysis of time series with missing values.

P S Wilson1, A C Tomsett, R Toumi

  • 1Space and Atmospheric Physics, Blackett Laboratory, Imperial College, London, SW7 2BW, United Kingdom. paul.wilson@imperial.ac.uk

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|August 26, 2003
PubMed
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Missing data complicates long memory estimation. Simple gap-filling methods like interpolation, random, and mean filling can distort time series analysis, but interpolation may work for persistent series with large gaps.

Area of Science:

  • Time Series Analysis
  • Statistical Modeling
  • Data Science

Background:

  • Accurate estimation of long memory is crucial in various fields.
  • Missing data presents a significant challenge in time series analysis.
  • Existing methods for handling missing data may impact long memory estimation.

Purpose of the Study:

  • To investigate the impact of common gap-filling techniques on long memory estimation.
  • To compare the performance of interpolation, random, and mean filling methods.
  • To identify reliable methods for long memory estimation in the presence of missing data.

Main Methods:

  • Numerical simulations were employed to assess the effects of gap-filling.
  • Three gap-filling techniques were tested: interpolation, random filling, and mean filling.

Related Experiment Videos

  • The study analyzed both persistent and antipersistent time series.

    • Main Results:

    • Gap-filling techniques introduce significant deviations in scaling behavior for both persistent and antipersistent time series.
    • The effectiveness of gap-filling methods varies depending on the time series characteristics and gap size.
    • Interpolation demonstrated potential reliability for persistent time series when gaps are smaller than the scale of interest.

    Conclusions:

    • Simple gap-filling methods can compromise the accuracy of long memory estimation.
    • Careful consideration of the chosen gap-filling technique is necessary for reliable time series analysis.
    • Interpolation may be a viable option for persistent time series under specific conditions.