Multivariate Multifractal Detrending Moving Average Analysis of Air Pollutants

Milena Kojić, Petar Mitić, Marko Dimovski and Jelena Minović
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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

Mathematics, 2021, vol. 9, issue 7, 1-17

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.

Keywords: multifractal detrended moving average; multivariate analysis; air pollutants (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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