IntLevPy: A Python library to classify and model intermittent and Lévy processes
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909770000105472 |
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| author | Bhandari, Shailendra Lencastre, Pedro Denysov, Sergiy Bystryk, Yurii Lind, Pedro G. |
| author_facet | Bhandari, Shailendra Lencastre, Pedro Denysov, Sergiy Bystryk, Yurii Lind, Pedro G. |
| contents | IntLevPy provides a comprehensive description of the IntLevPy Package, a Python library designed for simulating and analyzing intermittent and Lévy processes. The package includes functionalities for process simulation, including full parameter estimation and fitting optimization for both families of processes, moment calculation, and classification methods. The classification methodology utilizes adjusted-$R^2$ and a noble performance measure Γ, enabling the distinction between intermittent and Lévy processes. IntLevPy integrates iterative parameter optimization with simulation-based validation. This paper provides an in-depth user guide covering IntLevPy software architecture, installation, validation workflows, and usage examples. In this way, IntLevPy facilitates systematic exploration of these two broad classes of stochastic processes, bridging theoretical models and practical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03729 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IntLevPy: A Python library to classify and model intermittent and Lévy processes Bhandari, Shailendra Lencastre, Pedro Denysov, Sergiy Bystryk, Yurii Lind, Pedro G. Neural and Evolutionary Computing Mathematical Software IntLevPy provides a comprehensive description of the IntLevPy Package, a Python library designed for simulating and analyzing intermittent and Lévy processes. The package includes functionalities for process simulation, including full parameter estimation and fitting optimization for both families of processes, moment calculation, and classification methods. The classification methodology utilizes adjusted-$R^2$ and a noble performance measure Γ, enabling the distinction between intermittent and Lévy processes. IntLevPy integrates iterative parameter optimization with simulation-based validation. This paper provides an in-depth user guide covering IntLevPy software architecture, installation, validation workflows, and usage examples. In this way, IntLevPy facilitates systematic exploration of these two broad classes of stochastic processes, bridging theoretical models and practical applications. |
| title | IntLevPy: A Python library to classify and model intermittent and Lévy processes |
| topic | Neural and Evolutionary Computing Mathematical Software |
| url | https://arxiv.org/abs/2506.03729 |