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

2025-06-04

AI Summary

为解决间歇性和Lévy过程的分类与建模问题，IntLevPy提供Python库，通过参数估计、拟合优化、分类方法（如调整R²和Γ指标）及模拟验证实现。

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Graphical Models
• Intelligent Robots: State Estimation

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

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.

Problem

• Classify and model intermittent and Lévy processes
• Estimate parameters and optimize fitting for stochastic processes
• Distinguish between intermittent and Lévy processes using performance measures

Innovation

• Python library for intermittent and Lévy processes
• Parameter estimation and fitting optimization
• Adjusted-R² and Γ for process classification