Disentangling Sources of Multifractality in Time Series
This contribution addresses the question commonly asked in the scientific literature about the sources of multifractality in time series. Two primary sources are typically considered. These are temporal correlations and heavy tails in the distribution of fluctuations. Most often, they are treated as...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/2/205 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588083628867584 |
|---|---|
| author | Robert Kluszczyński Stanisław Drożdż Jarosław Kwapień Tomasz Stanisz Marcin Wątorek |
| author_facet | Robert Kluszczyński Stanisław Drożdż Jarosław Kwapień Tomasz Stanisz Marcin Wątorek |
| author_sort | Robert Kluszczyński |
| collection | DOAJ |
| description | This contribution addresses the question commonly asked in the scientific literature about the sources of multifractality in time series. Two primary sources are typically considered. These are temporal correlations and heavy tails in the distribution of fluctuations. Most often, they are treated as two independent components, while true multifractality cannot occur without temporal correlations. The distributions of fluctuations affect the span of the multifractal spectrum only when correlations are present. These issues are illustrated here using series generated by several model mathematical cascades, which by design build correlations into these series. The thickness of the tails of fluctuations in such series is then governed by an appropriate procedure of adjusting them to q-Gaussian distributions, and q is treated as a variable parameter that, while preserving correlations, allows for tuning these distributions to the desired functional form. Multifractal detrended fluctuation analysis (MFDFA), as the most commonly used practical method for quantifying multifractality, is then used to identify the influence of the thickness of the fluctuation tails in the presence of temporal correlations on the width of multifractal spectra. The obtained results point to the Gaussian distribution, so <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>q</mi><mo>=</mo><mn>1</mn></mrow></semantics></math></inline-formula>, as the appropriate reference distribution to evaluate the contribution of fatter tails to the width of multifractal spectra. An appropriate procedure is presented to make such estimates. |
| format | Article |
| id | doaj-art-b767cacd522b4144a5c714309d5b88f6 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-b767cacd522b4144a5c714309d5b88f62025-01-24T13:39:44ZengMDPI AGMathematics2227-73902025-01-0113220510.3390/math13020205Disentangling Sources of Multifractality in Time SeriesRobert Kluszczyński0Stanisław Drożdż1Jarosław Kwapień2Tomasz Stanisz3Marcin Wątorek4Complex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, ul. Radzikowskiego 152, 31-342 Kraków, PolandComplex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, ul. Radzikowskiego 152, 31-342 Kraków, PolandComplex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, ul. Radzikowskiego 152, 31-342 Kraków, PolandComplex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, ul. Radzikowskiego 152, 31-342 Kraków, PolandFaculty of Computer Science and Telecommunications, Cracow University of Technology, 31-155 Kraków, PolandThis contribution addresses the question commonly asked in the scientific literature about the sources of multifractality in time series. Two primary sources are typically considered. These are temporal correlations and heavy tails in the distribution of fluctuations. Most often, they are treated as two independent components, while true multifractality cannot occur without temporal correlations. The distributions of fluctuations affect the span of the multifractal spectrum only when correlations are present. These issues are illustrated here using series generated by several model mathematical cascades, which by design build correlations into these series. The thickness of the tails of fluctuations in such series is then governed by an appropriate procedure of adjusting them to q-Gaussian distributions, and q is treated as a variable parameter that, while preserving correlations, allows for tuning these distributions to the desired functional form. Multifractal detrended fluctuation analysis (MFDFA), as the most commonly used practical method for quantifying multifractality, is then used to identify the influence of the thickness of the fluctuation tails in the presence of temporal correlations on the width of multifractal spectra. The obtained results point to the Gaussian distribution, so <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>q</mi><mo>=</mo><mn>1</mn></mrow></semantics></math></inline-formula>, as the appropriate reference distribution to evaluate the contribution of fatter tails to the width of multifractal spectra. An appropriate procedure is presented to make such estimates.https://www.mdpi.com/2227-7390/13/2/205complexitytime series analysismathematical cascadesnonlinear correlationsmultifractalitysingularity spectra |
| spellingShingle | Robert Kluszczyński Stanisław Drożdż Jarosław Kwapień Tomasz Stanisz Marcin Wątorek Disentangling Sources of Multifractality in Time Series Mathematics complexity time series analysis mathematical cascades nonlinear correlations multifractality singularity spectra |
| title | Disentangling Sources of Multifractality in Time Series |
| title_full | Disentangling Sources of Multifractality in Time Series |
| title_fullStr | Disentangling Sources of Multifractality in Time Series |
| title_full_unstemmed | Disentangling Sources of Multifractality in Time Series |
| title_short | Disentangling Sources of Multifractality in Time Series |
| title_sort | disentangling sources of multifractality in time series |
| topic | complexity time series analysis mathematical cascades nonlinear correlations multifractality singularity spectra |
| url | https://www.mdpi.com/2227-7390/13/2/205 |
| work_keys_str_mv | AT robertkluszczynski disentanglingsourcesofmultifractalityintimeseries AT stanisławdrozdz disentanglingsourcesofmultifractalityintimeseries AT jarosławkwapien disentanglingsourcesofmultifractalityintimeseries AT tomaszstanisz disentanglingsourcesofmultifractalityintimeseries AT marcinwatorek disentanglingsourcesofmultifractalityintimeseries |