Multifractal scaling properties of daily air temperature time series

Kalamaras, Nikolaos; Philippopoulos, Kostas; Deligiorgi, Despina; Tzanis, Chris G.; Karvounis, George

doi:10.1016/j.chaos.2017.03.003

Cited by 48 publications

(27 citation statements)

References 38 publications

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“…This is similar to the type of scaling one sees, for instance, in turbulence data [7,8]. Power law type scaling behavior has been observed in many other physical systems also, see for instance [9,10]. See also [11] for a comprehensive overview of such modeling and discussions of various applications and estimation approaches.…”

Section: Introductionsupporting

confidence: 73%

Emergence of turbulent epochs in oil prices

Garnier

Sølna

2019

Chaos, Solitons & Fractals

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Oil price data have a complicated multi-scale structure that may vary with time. We use time-frequency analysis to identify the main features of these variations and, in particular, the regime shifts. The analysis is based on a wavelet-based decomposition and analysis of the associated scale spectrum. The joint estimation of the local Hurst exponent and volatility is the key to detect and identify regime shifting and switching of the oil price. The framework involves in particular modeling in terms of a process of "multi-fractional" type so that both the roughness and the volatility of the price process may vary with time. Special epochs then emerge as a result of these degrees of freedom, moreover, as a result of the special type of spectral estimator used. These special epochs are discussed and related to historical events. Some of them are not detected by standard analysis based on maximum likelihood estimation. The paper presents a novel algorithm for robust detection of such special epochs and multifractional behavior in financial or other types of data. In the financial context insight about such behavior of the asset price is important to evaluate financial contracts involving the asset.

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Section: Introductionsupporting

confidence: 73%

Emergence of turbulent epochs in oil prices

Garnier

Sølna

2019

Chaos, Solitons & Fractals

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“…erefore, the multifractal properties of stock markets [9][10][11][12][13], foreign exchange markets [14][15][16], bitcoin markets [17], atmospheric sciences [18], and phase transitions [19] have been studied previously. Recently, multifractal analysis was used to study the daily air temperature time series [20]. Moreover, MF-DFA is also an efficient method in analyzing the human heart rate time series [21].…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Implications of “Haze Special Law” on Air Quality in South Korea

2020

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In this work, the air pollution index in three cities (Seoul, Busan, and Daegu) in South Korea was studied using multifractal detrended fluctuation analysis (MF-DFA). Hurst, Renyi, and Holder exponents were used to analyze the characteristics of the concentration time series of PM 2.5 and NO 2 . e results showed that multifractality exists in each season interval and the multifractal degree of PM 2.5 is stronger than that of NO 2 . To investigate the effects of the implementation of the "haze special law" on February 15, 2019, we analyzed the time series of PM 2.5 during the time periods . We found that the multifractal spectrum width after the implementation of the law was narrower than that before the law for all the cities, which shows that the enactment of the law has played a role in improving the efficiency of air pollution control in South Korea. We also conclude that the major effects of the law will be particularly visible in larger cities. To study the main causes of multifractality, the shuffled and phase-randomized series were analyzed using MF-DFA, and the results demonstrated that the fat-tailed distribution resulted in the multifractality of the time series before and after the implementation of the "haze special law" in Seoul and Daegu, whereas long-range correlation resulted in multifractality of the series before and after the implementation of the law in Busan.

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“…A thorough presentation of multifractal statistics and an application of nonlinear dynamics to weather and climate is presented by Lovejoy and Schertzer [35]. In general, meteorological time series have a multifractal structure and the MF-DFA has been used for the analysis of temperature time series [36], precipitation amount data [37][38][39], wind speed records [40][41][42], climate studies [43,44], agrometeorological data [45,46], particulate matter data [47], and paleoclimatic records [48], among others.…”

Section: Introductionmentioning

confidence: 99%

Multifractal Detrended Fluctuation Analysis of Temperature Reanalysis Data over Greece

et al. 2019

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The Multifractal Detrended Fluctuation Analysis (MF-DFA) is used to examine the scaling behavior and the multifractal characteristics of the mean daily temperature time series of the ERA-Interim reanalysis data for a domain centered over Greece. The results showed that the time series from all grid points exhibit the same behavior: they have a positive long-term correlation and their multifractal structure is insensitive to local fluctuations with a large magnitude. Special emphasis was given to the spatial distribution of the main characteristics of the multifractal spectrum: the value of the Hölder exponent, the spectral width, the asymmetry, and the truncation type of the spectra. The most interesting finding is that the spatial distribution of almost all spectral parameters is decisively determined by the land-sea distribution. The results could be useful in climate research for examining the reproducibility of the nonlinear dynamics of reanalysis datasets and model outputs.

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Multifractal scaling properties of daily air temperature time series

Cited by 48 publications

References 38 publications

Emergence of turbulent epochs in oil prices

Emergence of turbulent epochs in oil prices

Investigation of the Implications of “Haze Special Law” on Air Quality in South Korea

Multifractal Detrended Fluctuation Analysis of Temperature Reanalysis Data over Greece

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