Simulation and Analysis of Multifractional Stochastic Processes with R Package Rmfrac
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915908765614080 |
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| author | Olenko, Andriy Samarakoon, Nemini |
| author_facet | Olenko, Andriy Samarakoon, Nemini |
| contents | Brownian motion and fractional Brownian motion have been widely applied in statistical modeling in finance, telecommunication, network traffic, neuroscience, physics, and other fields. More realistic models for real time series data, such as multifractional processes, generalize these classical models by allowing their regularity to vary over time. A new class of Gaussian Haar-based multifractional processes, which utilizes the Haar wavelet series representation, was recently introduced. It significantly extends the range of available models by incorporating more general classes of Hurst functions. The Rmfrac package was developed to simulate multifractional time series. The package also comprises several functions for the analysis and visualization of time series. It includes the estimation of the Hurst function and local fractal dimension, clustering realizations and computing various geometric statistics of these time series. The package also offers a Shiny application to visualize simulation and estimation results. The article presents an overview of the Rmfrac package and exemplifies its main functionalities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_01580 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Simulation and Analysis of Multifractional Stochastic Processes with R Package Rmfrac Olenko, Andriy Samarakoon, Nemini Computation Probability 60G22, 62M20, 62P20 Brownian motion and fractional Brownian motion have been widely applied in statistical modeling in finance, telecommunication, network traffic, neuroscience, physics, and other fields. More realistic models for real time series data, such as multifractional processes, generalize these classical models by allowing their regularity to vary over time. A new class of Gaussian Haar-based multifractional processes, which utilizes the Haar wavelet series representation, was recently introduced. It significantly extends the range of available models by incorporating more general classes of Hurst functions. The Rmfrac package was developed to simulate multifractional time series. The package also comprises several functions for the analysis and visualization of time series. It includes the estimation of the Hurst function and local fractal dimension, clustering realizations and computing various geometric statistics of these time series. The package also offers a Shiny application to visualize simulation and estimation results. The article presents an overview of the Rmfrac package and exemplifies its main functionalities. |
| title | Simulation and Analysis of Multifractional Stochastic Processes with R Package Rmfrac |
| topic | Computation Probability 60G22, 62M20, 62P20 |
| url | https://arxiv.org/abs/2604.01580 |