PoPPy: A Point Process Toolbox Based on PyTorch

TL;DR

PoPPy is a PyTorch-based toolbox that enables flexible design, efficient learning, and analysis of point process models for sequential data, supporting tasks like causality analysis, simulation, and prediction.

Contribution

It introduces a user-friendly, scalable toolkit for designing and learning point process models, facilitating interpretable analysis and large-scale applications.

Findings

Supports Granger causality analysis of multivariate point processes

Enables efficient simulation and prediction of event sequences

Provides a flexible platform for modeling complex sequential data

Abstract

PoPPy is a Point Process toolbox based on PyTorch, which achieves flexible designing and efficient learning of point process models. It can be used for interpretable sequential data modeling and analysis, e.g., Granger causality analysis of multi-variate point processes, point process-based simulation and prediction of event sequences. In practice, the key points of point process-based sequential data modeling include: 1) How to design intensity functions to describe the mechanism behind observed data? 2) How to learn the proposed intensity functions from observed data? The goal of PoPPy is providing a user-friendly solution to the key points above and achieving large-scale point process-based sequential data analysis, simulation and prediction.