Point Process Flows

Abstract

Event sequences can be modeled by temporal point processes (TPPs) to capture their asynchronous and probabilistic nature. We propose an intensity-free framework that directly models the point process distribution by utilizing normalizing flows. This approach is capable of capturing highly complex temporal distributions and does not rely on restrictive parametric forms. Comparisons with state-of-the-art baseline models on both synthetic and challenging real-life datasets show that the proposed framework is effective at modeling the stochasticity of discrete event sequences.

References in corpus (6)

• Auto-Encoding Variational Bayes
• NICE: Non-linear Independent Components Estimation
• Variational Inference with Normalizing Flows
• Normalizing Flows: An Introduction and Review of Current Methods
• Learning Temporal Point Processes via Reinforcement Learning
• Time Perception Machine: Temporal Point Processes for the When, Where and What of Activity Prediction

Cited by in corpus (2)

• Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
• Neural Spatio-Temporal Point Processes