Modeling and Applications for Temporal Point Processes

Abstract

Real-world entities' behaviors, associated with their side information, are often recorded over time as asynchronous event sequences. Such event sequences are the basis of many practical applications, neural spiking train study, earth quack prediction, crime analysis, infectious disease diffusion forecasting, condition-based preventative maintenance, information retrieval and behavior-based network analysis and services, etc. Temporal point process (TPP) is a principled mathematical tool for the modeling and learning of asynchronous event sequences, which captures the instantaneous happening rate of the events and the temporal dependency between historical and current events. TPP provides us with an interpretable model to describe the generative mechanism of event sequences, which is beneficial for event prediction and causality analysis. Recently, it has been shown that TPP has potentials to many machine learning and data science applications and can be combined with other cutting-edge machine learning techniques like deep learning, reinforcement learning, adversarial learning, and so on. We will start with an elementary introduction of TPP model, including the basic concepts of the model, the simulation method of event sequences; in the second part of the tutorial, we will introduce typical TPP models and their traditional learning methods; in the third part of the tutorial, we will discuss the recent progress on the modeling and learning of TPP, including neural network-based TPP models, generative adversarial networks (GANs) for TPP, and deep reinforcement learning of TPP. We will further talk about the practical application of TPP, including useful data augmentation methods for learning from imperfect observations, typical applications and examples like healthcare and industry maintenance, and existing open source toolboxes.