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Multivariate Multifractal Detrending Moving Average Analysis of Air Pollutants

Author

Listed:
  • Milena Kojić

    (Institute of Economic Sciences, 11000 Belgrade, Serbia)

  • Petar Mitić

    (Institute of Economic Sciences, 11000 Belgrade, Serbia)

  • Marko Dimovski

    (EVN AD, 1000 Skopje, North Macedonia)

  • Jelena Minović

    (Institute of Economic Sciences, 11000 Belgrade, Serbia)

Abstract

One of the most challenging endeavors of contemporary research is to describe and analyze the dynamic behavior of time series arising from real-world systems. To address the need for analyzing long-range correlations and multifractal properties of multivariate time series, we generalize the multifractal detrended moving average algorithm (MFDMA) to the multivariate case and propose a multivariate MFDMA algorithm (MV-MFDMA). The validity and performance of the proposed algorithm are tested by conducting numerical simulations on synthetic multivariate monofractal and multifractal time series. The MV-MFDMA algorithm is then utilized to analyze raw, seasonally adjusted, and remainder components of five air pollutant time series. Results from all three cases reveal multifractal properties with persistent long-range correlations.

Suggested Citation

  • Milena Kojić & Petar Mitić & Marko Dimovski & Jelena Minović, 2021. "Multivariate Multifractal Detrending Moving Average Analysis of Air Pollutants," Mathematics, MDPI, vol. 9(7), pages 1-17, March.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:7:p:711-:d:523931
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    References listed on IDEAS

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