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

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 PM2.5 and NO2. The results showed that multifractality exists in each season interval and the multifractal degree of PM2.5 is stronger than that of NO2. To investigate the effects of the implementation of the “haze special law” on February 15, 2019, we analyzed the time series of PM2.5 during the time periods from February 15, 2018, to December 16, 2018, and February 15, 2019, to December 16, 2019. 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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Evaluation of the training objectives with surface electromyography

Bio-Algorithms and Med-Systems ◽

10.1515/bams-2015-0035 ◽

2016 ◽

Vol 12 (1) ◽

Author(s):

Paulina Trybek ◽

Michał Nowakowski ◽

Lukasz Machura

Keyword(s):

Surface Electromyography ◽

Multifractal Spectrum ◽

Base Case ◽

Fluctuation Analysis ◽

Multifractal Detrended Fluctuation Analysis ◽

Analysis Technique ◽

Before And After ◽

Electromyographic Signal ◽

Surface Electromyographic Signal ◽

Laparoscopic Tools

AbstractIn this work, the multifractal analysis of the kinesiological surface electromyographic signal is proposed. The goal was to investigate the level of neuromuscular activation during complex movements on the laparoscopic trainer. The basic issue of this work concerns the changes observed in the signal obtained from the complete beginner in the field of using laparoscopic tools and the same person subjected to the series of training. To quantify the complexity of the kinesiological surface electromyography, the nonlinear analysis technique, namely, the multifractal detrended fluctuation analysis, was adopted. The analysis was based on the parameters describing the multifractal spectrum – the Hurst exponent – and the spectrum width. The statistically significant differences for a selected group of muscles at the different states (before and after training) are presented. In addition, as the base case, the relaxation state was considered and compared with the working states.

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Distribution of Air Temperature Multifractal Characteristics Over Greece

Atmosphere ◽

10.3390/atmos10020045 ◽

2019 ◽

Vol 10 (2) ◽

pp. 45 ◽

Cited By ~ 7

Author(s):

Nikolaos Kalamaras ◽

Chris Tzanis ◽

Despina Deligiorgi ◽

Kostas Philippopoulos ◽

Ioannis Koutsogiannis

Keyword(s):

Time Series ◽

Multifractal Spectrum ◽

Weather Conditions ◽

Fluctuation Analysis ◽

Spatial Distributions ◽

Multifractal Detrended Fluctuation Analysis ◽

Natural Processes ◽

Complex Interactions ◽

Local Fluctuations ◽

Almost All

In this study, Multifractal Detrended Fluctuation Analysis (MF-DFA) is applied to daily temperature time series (mean, maximum and minimum values) from 22 Greek meteorological stations with the purpose of examining firstly their scaling behavior and then checking if there are any differences in their multifractal characteristics. The results showed that the behavior is the same at almost all stations, i.e., time series are positive long-term correlated and their multifractal structure is insensitive to local fluctuations with large magnitude. Moreover, this study deals with the spatial distribution of the main characteristics of multifractal (singularity) spectrum: the dominant Hurst exponent, the width of the spectrum, the asymmetry and the truncation type of the spectrum. The spatial distributions are discussed in terms of possible effects from various climatic features. In general, local atmospheric circulation and weather conditions are found to affect the shape of the spectrum and the corresponding spatial distributions. Furthermore, the intercorrelation of the main multifractal spectrum parameters resulted in a well-defined group of stations sharing similar multifractal characteristics. The results indicate the usefulness of the non-linear analysis in climate research due to the complex interactions among the natural processes.

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Multifractality Nature of Microtubule Dynamic Instability Process

10.1101/2020.05.28.121749 ◽

2020 ◽

Author(s):

Vahid Rezania ◽

Ferry C. Sudirga ◽

Jack A. Tuszynski

Keyword(s):

Experimental Data ◽

Time Series ◽

Dynamic Instability ◽

Simulated Data ◽

Multifractal Spectrum ◽

Mathematical Representation ◽

Fluctuation Analysis ◽

Multifractal Detrended Fluctuation Analysis ◽

Synthetic Time Series ◽

First Time

AbstractThe irregularity of growing and shortening patterns observed experimentally in microtubules reflects a dynamical system that fluctuates stochastically between assembly and disassembly phases. The observed time series of microtubule lengths have been extensively analyzed to shed light on structural and dynamical properties of microtubules. Here, for the first time, Multifractal Detrended Fluctuation analysis (MFDFA) has been employed to investigate the multifractal and topological properties of both experimental and simulated microtubule time series. We find that the time dependence of microtubule length possesses true multifractal characteristics and cannot be described by monofractal distributions. Based on the multifractal spectrum profile, a set of multifractal indices have been calculated that can be related to the level of dynamical activities of microtubules. We also show that the resulting multifractal spectra for the simulated data might not be comparable with experimental data.Statement of SignificanceMicrotubules are some of the most important subcellular structures involved in a multitude of functions in all eukaryotic cells. In addition to their cylindrical geometry, their polymerization/depolymerization dynamics, termed dynamic instability, is unique among all protein polymers. In this paper we demonstrate that there is a very specific mathematical representation of microtubule growth and shrinkage time series in terms of multifractality. We further show that using this characteristic, one can distinguish real experimental data from synthetic time series generated from computer simulations.

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Multifractal Detrended Fluctuation Analysis of Temperature Reanalysis Data over Greece

Atmosphere ◽

10.3390/atmos10060336 ◽

2019 ◽

Vol 10 (6) ◽

pp. 336 ◽

Cited By ~ 6

Author(s):

Kostas Philippopoulos ◽

Nikolaos Kalamaras ◽

Chris G. Tzanis ◽

Despina Deligiorgi ◽

Ioannis Koutsogiannis

Keyword(s):

Time Series ◽

Spatial Distribution ◽

Detrended Fluctuation Analysis ◽

Multifractal Spectrum ◽

Spectral Width ◽

Reanalysis Data ◽

Fluctuation Analysis ◽

Spectral Parameters ◽

Multifractal Detrended Fluctuation Analysis ◽

Detrended Fluctuation

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 Cross Correlation Analysis of Agro-Meteorological Datasets (Including Reference Evapotranspiration) of California, United States

Atmosphere ◽

10.3390/atmos11101116 ◽

2020 ◽

Vol 11 (10) ◽

pp. 1116

Author(s):

Adarsh Sankaran ◽

Jaromir Krzyszczak ◽

Piotr Baranowski ◽

Archana Devarajan Sindhu ◽

Nandhineekrishna Kumar ◽

...

Keyword(s):

Time Series ◽

Cross Correlation ◽

Reference Evapotranspiration ◽

Multifractal Spectrum ◽

Fluctuation Analysis ◽

Scaling Exponent ◽

Scaling Exponents ◽

Multifractal Detrended Fluctuation Analysis ◽

Cross Correlation Analysis ◽

The Individual

The multifractal properties of six acknowledged agro-meteorological parameters, such as reference evapotranspiration (ET0), wind speed (U), incoming solar radiation (SR), air temperature (T), air pressure (P), and relative air humidity (RH) of five stations in California, USA were examined. The investigation of multifractality of datasets from stations with differing terrain conditions using the Multifractal Detrended Fluctuation Analysis (MFDFA) showed the existence of a long-term persistence and multifractality irrespective of the location. The scaling exponents of SR and T time series are found to be higher for stations with higher altitudes. Subsequently, this study proposed using the novel multifractal cross correlation (MFCCA) method to examine the multiscale-multifractal correlations properties between ET0 and other investigated variables. The MFCCA could successfully capture the scale dependent association of different variables and the dynamics in the nature of their associations from weekly to inter-annual time scales. The multifractal exponents of P and U are consistently lower than the exponents of ET0, irrespective of station location. This study found that joint scaling exponent was nearly the average of scaling exponents of individual series in different pairs of variables. Additionally, the α-values of joint multifractal spectrum were lower than the α values of both of the individual spectra, validating two universal properties in the MFCCA studies for agro-meteorological time series. The temporal evolution of cross-correlation determined by the MFCCA successfully captured the dynamics in the nature of associations in the P-ET0 link.

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A multifractal detrended fluctuation analysis of housing market: a role of financial crises in developed economies

International Journal of Housing Markets and Analysis ◽

10.1108/ijhma-06-2021-0068 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Syed Ali Raza ◽

Nida Shah ◽

Muhammad Tahir Suleman ◽

Md Al Mamun

Keyword(s):

Financial Crisis ◽

Housing Market ◽

Market Efficiency ◽

House Price ◽

Fluctuation Analysis ◽

Content Type ◽

Multifractal Detrended Fluctuation Analysis ◽

Long Run ◽

Before And After

Purpose This study aims to examine the house price fluctuations in G7 countries by using the multifractal detrended fluctuation analysis (MF-DFA) for the years 1970–2019. The study examined the market efficiency between the short-term and long-term in the full sample period, before and after the global financial crisis period. Design/methodology/approach This study uses the MF-DFA to analyze house price fluctuations. Findings The findings confirmed that the housing market series are multifractal. Furthermore, all the markets showed long-term persistence in both the short and long-term. The USA is identified as the most persistent house market in the short run and Japan in the long run. Moreover, in terms of efficiency, Canada is identified as the most efficient house market in the long run and the UK in the short run. Finally, the result of before and after the financial crisis period is consistent with the full sample result. Originality/value The contribution of this study in the literature is fourfold. This is the first study that has examined the house prices efficiency by using the MF-DFA technique given by Kantelhardt et al. (2002). Previously, the house market prices and efficiency has been investigated using generalized Hurst exponent (Liu et al., 2019), Quantile Regression Approach (Chae and Bera, 2019; Tiwari et al., 2019) but no study to the best of the knowledge has been done that has used the MF-DFA technique on the housing market. Second, this is the first study that has focused on the house markets of G7 countries. Third, this study explores the house market efficiency by dividing the market into two periods i.e. before and after the financial crisis. The study strives to investigate if the financial crisis determines the change in the degree of market efficiency or not. Finally, the study gives valuable insights to the investors that will help them in their investment decisions.

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Multifractal Analysis of Hydrologic Data Using Wavelet Methods and Fluctuation Analysis

Discrete Dynamics in Nature and Society ◽

10.1155/2017/3148257 ◽

2017 ◽

Vol 2017 ◽

pp. 1-18 ◽

Cited By ~ 3

Author(s):

Tongzhou Zhao ◽

Liang Wu ◽

Dehua Li ◽

Yiming Ding

Keyword(s):

Time Series ◽

Water Level ◽

Multifractal Analysis ◽

Simulation Models ◽

Northern China ◽

Fluctuation Analysis ◽

Multifractal Detrended Fluctuation Analysis ◽

Wavelet Leaders ◽

Detailed Characteristic ◽

Wavelet Methods

We study the multifractal properties of water level with a high-frequency and massive time series using wavelet methods (estimation of Hurst exponents, multiscale diagram, and wavelet leaders for multifractal analysis (WLMF)) and multifractal detrended fluctuation analysis (MF-DFA). The dataset contains more than two million records from 10 observation sites at a northern China river. The multiscale behaviour is observed in this time series, which indicates the multifractality. This multifractality is detected via multiscale diagram. Then we focus on the multifractal analysis using MF-DFA and WLMF. The two methods give the same conclusion that at most sites the records satisfy the generalized binomial multifractal model, which is robust for different times (morning, afternoon, and evening). The variation in the detailed characteristic parameters of the multifractal model indicates that both human activities and tributaries influence the multifractality. Our work is useful for building simulation models of the water level of local rivers with many observation sites.

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Investigating Long-Range Dependence of Emerging Asian Stock Markets Using Multifractal Detrended Fluctuation Analysis

Symmetry ◽

10.3390/sym12071157 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1157

Author(s):

Faheem Aslam ◽

Saima Latif ◽

Paulo Ferreira

Keyword(s):

Time Series ◽

Long Range ◽

Stock Markets ◽

Detrended Fluctuation Analysis ◽

Financial Time Series ◽

Long Range Dependence ◽

Fluctuation Analysis ◽

Multifractal Detrended Fluctuation Analysis ◽

Financial Time ◽

Detrended Fluctuation

The use of multifractal approaches has been growing because of the capacity of these tools to analyze complex properties and possible nonlinear structures such as those in financial time series. This paper analyzes the presence of long-range dependence and multifractal parameters in the stock indices of nine MSCI emerging Asian economies. Multifractal Detrended Fluctuation Analysis (MFDFA) is used, with prior application of the Seasonal and Trend Decomposition using the Loess (STL) method for more reliable results, as STL separates different components of the time series and removes seasonal oscillations. We find a varying degree of multifractality in all the markets considered, implying that they exhibit long-range correlations, which could be related to verification of the fractal market hypothesis. The evidence of multifractality reveals symmetry in the variation trends of the multifractal spectrum parameters of financial time series, which could be useful to develop portfolio management. Based on the degree of multifractality, the Chinese and South Korean markets exhibit the least long-range dependence, followed by Pakistan, Indonesia, and Thailand. On the contrary, the Indian and Malaysian stock markets are found to have the highest level of dependence. This evidence could be related to possible market inefficiencies, implying the possibility of institutional investors using active trading strategies in order to make their portfolios more profitable.

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Modeling and forecasting multifractal wavelet scale: Western market versus eastern market

International Journal of Modern Physics C ◽

10.1142/s0129183118501097 ◽

2018 ◽

Vol 29 (11) ◽

pp. 1850109 ◽

Cited By ~ 1

Author(s):

Emrah Oral ◽

Gazanfer Unal

Keyword(s):

Time Series ◽

Time Series Data ◽

Moving Average ◽

Price Change ◽

Wavelet Coherence ◽

Series Data ◽

Primary Study ◽

Fluctuation Analysis ◽

Multifractal Detrended Fluctuation Analysis ◽

Time Frequency

This leading primary study is about modeling multifractal wavelet scale time series data using multiple wavelet coherence (MWC), continuous wavelet transform (CWT) and multifractal detrended fluctuation analysis (MFDFA) and forecasting with vector autoregressive fractionally integrated moving average (VARFIMA) model. The data is acquired from Yahoo Finances!, which is composed of 1671 daily stock market of eastern (NIKKEI, TAIEX, KOPSI) and western (SP500, FTSE, DAX) markets. Once the co-movement dependencies on time-frequency space are determined with MWC, the coherent data is extracted out of raw data at a certain scale by using CWT. The multifractal behavior of the extracted series is verified by MFDFA and its local Hurst exponents have been calculated obtaining root mean square of residuals at each scale. This inter-calculated fluctuation function time series has been re-scaled and used to estimate the process with VARFIMA model and forecasted accordingly. The results have shown that the direction of price change is determined without difficulty and the efficiency of forecasting has been substantially increased using highly correlated multifractal wavelet scale time series data.

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