PoPPy: A Point Process Toolbox Based on PyTorch
This provides a user-friendly solution for researchers and practitioners working with interpretable sequential data analysis, though it is incremental as it builds on existing point process methods with a new implementation.
The authors tackled the problem of designing and learning point process models for sequential data by introducing PoPPy, a PyTorch-based toolbox that enables flexible modeling and efficient analysis, including Granger causality and event sequence simulation.
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.