IntLevPy: A Python library to classify and model intermittent and L\'evy processes

TL;DR

IntLevPy is a Python library that enables simulation, parameter estimation, classification, and analysis of intermittent and Lévy processes, bridging theoretical models with practical applications.

Contribution

The paper introduces IntLevPy, a comprehensive Python package for simulating, estimating, and classifying intermittent and Lévy processes, with novel classification methods and validation workflows.

Findings

Effective process classification using adjusted-R^2 and {\

}Gamma{\

Abstract

IntLevPy provides a comprehensive description of the IntLevPy Package, a Python library designed for simulating and analyzing intermittent and L\'evy 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 {\Gamma}, enabling the distinction between intermittent and L\'evy 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.