MFDFA: Efficient multifractal detrended fluctuation analysis in python

Gorjão, Leonardo Rydin; Hassan, Galib; Kurths, Jürgen; Witthaut, Dirk

doi:10.1016/j.cpc.2021.108254

Cited by 33 publications

(17 citation statements)

References 65 publications

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“…Second, the step size for each forward movement is determined. Therefore, the number of movement times is calculated by subtracting the moving window size from the total length of the time series data (Zhang et al 2019 ; Gorjão et al 2022 ).…”

Section: Methodsmentioning

confidence: 99%

The aggregate and sectoral time-varying market efficiency during crisis periods in Turkey: a comparative analysis with COVID-19 outbreak and the global financial crisis

Erer¹,

Erer

Güngör

2023

Financ Innov

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This study aims to examine the time-varying efficiency of the Turkish stock market’s major stock index and eight sectoral indices, including the industrial, financial, service, information technology, basic metals, tourism, real estate investment, and chemical petrol plastic, during the COVID-19 outbreak and the global financial crisis (GFC) within the framework of the adaptive market hypothesis. This study employs multifractal detrended fluctuation analysis to illustrate these sectors’ multifractality and short- and long-term dependence. The results show that all sectoral returns have greater persistence during the COVID-19 outbreak than during the GFC. Second, the real estate and information technology industries had the lowest levels of efficiency during the GFC and the COVID-19 outbreak. Lastly, the fat-tailed distribution has a greater effect on multifractality in these industries. Our results validate the conclusions of the adaptive market hypothesis, according to which arbitrage opportunities vary over time, and contribute to policy formulation for future outbreak-induced economic crises.

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Section: Methodsmentioning

confidence: 99%

The aggregate and sectoral time-varying market efficiency during crisis periods in Turkey: a comparative analysis with COVID-19 outbreak and the global financial crisis

Erer¹,

Erer

Güngör

2023

Financ Innov

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“…Hence, the Multifractal Detrended Fluctuation Analysis (MF-DFA) [10] effectively identifies and quantifies multifractal and scaling characteristics in non-stationary time series. MF-DFA has found widespread success in various research domains, including Pattern Recognition [27], Neurology [28], Econophysics [13], and numerous other fields [9,29].…”

Section: Methodsmentioning

confidence: 99%

The (in)efficiency of USA Education Group stocks: before, during and after COVID-19

Fernandes,

Silva

et al. 2024

Preprint

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This paper represents a pioneering effort to investigate multifractal dynamics that exclusively encompass the return time series of USA Education Group Stocks concerning two non-overlapping periods (before, during,. Given this, we employ the Multifractal Detrended Fluctuations Analysis (MF-DFA). In this sense, we investigate the generalized Hurst exponent ℎ(𝑞) and the Rényi exponent 𝜏 (𝑞) for each asset and quantify their statistical properties, which allowed us to observe separately the contributing small scale (primarily via the negative moments 𝑞) and the large scale (via the positive moments 𝑞). We perform a fourth-degree polynomial regression fit to estimate the complexity parameters that describe the degree of multifractality of the underlying process. Also, we shall apply the inefficiency multifractal index to assess the COVID-19 shock for both periods. Our findings show that for both periods, the majority of these assets are marked by multifractal dynamics associated with persistent behaviour (𝛼 0 > 0.5), a higher degree of multifractality and the dominance of large fluctuations. At the same time, most of these assets show asymmetry parameter (𝑅 > 1) for both periods, indicating that large fluctuations contributed more to multifractality in the time series of returns.

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“…All analysis was performed in Python 3.7. Detrended fluctuation analysis was performed by the library MFDFA 40 . The main statistical libraries used were Statsmodels 41 and Scikit‐learn 42…”

Section: Methodsmentioning

confidence: 99%

Wrist accelerometer temporal analysis as a prognostic tool for aged care residents: A sub‐study of the ReMInDAR trial

Andrade

Lim

Kelly

et al. 2022

J American Geriatrics Society

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Background Objective measures for screening, prioritizing, and planning care for frail individuals are essential for appropriate aged care provision. This study evaluates metrics derived from actigraphy measures (captured by wrist accelerometer) as a digital biomarker to identify frail individuals at risk of adverse outcomes, including death, hospitalization, and cognitive decline. Methods This was a secondary study using data from a randomized controlled trial assessing the effectiveness of an ongoing pharmacist service in residential aged care facilities. Three metrics are studied and compared: the Frailty Index, the daily time spent in light time activity, and the temporal correlation of the actigraphy signal, measured by detrended fluctuation analysis. The association between actigraphy‐derived metrics at baseline and adverse events within 12 months (death, cognitive decline, and hospitalizations) was assessed using logistic regression. Results Actigraphy records were available for 213 participants living in aged‐care, median age of 85 years. Individuals with higher temporal correlation (activity is less random) were at lower risk of death (Standardized OR: 0.49; 95% CI 0.34, 0.7, p < 0.001) and hospitalization (Standardized OR: 0.57; 95% CI 0.42, 0.77, p < 0.001) in 12 months, but there was no difference in cognitive decline (Standardized OR: 1; 95% CI 0.74, 1.35, p = 0.98). The predictive model that included temporal correlation had an area under the curve of 0.70 (CI 0.60–0.80) for death and 0.64 (CI 0.54–0.72) for hospitalization. Conclusion Temporal correlation of the actigraphy signal from aged care residents was strongly associated with death and hospitalization, but not cognitive decline. Digital biomarkers may have a place as an objective, accurate, and low‐cost patient metric to support risk stratification and clinical planning.

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MFDFA: Efficient multifractal detrended fluctuation analysis in python

Cited by 33 publications

References 65 publications

The aggregate and sectoral time-varying market efficiency during crisis periods in Turkey: a comparative analysis with COVID-19 outbreak and the global financial crisis

The aggregate and sectoral time-varying market efficiency during crisis periods in Turkey: a comparative analysis with COVID-19 outbreak and the global financial crisis

The (in)efficiency of USA Education Group stocks: before, during and after COVID-19

Wrist accelerometer temporal analysis as a prognostic tool for aged care residents: A sub‐study of the ReMInDAR trial

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