Guest Editorial Robust Learning of Spatio-Temporal Point Processes: Modeling, Algorithm, and Applications

Temporal data are ubiquitous in real-world applications, and they can be generally divided into two categories: 1) synchronous temporal data which are basically equivalent to time series data; and 2) the asynchronous data which are often in the form of event data with a time stamp in continuous time-space. In fact, the event data are often converted to the time series by aggregating the event count in equal time intervals in many previous approaches. While it is often of one's greater interest to directly establish models based on the raw event data whose time stamps carry useful information, especially for those time-sensitive tasks, ranging from earthquake prediction, crime analysis, to infectious disease diffusion forecasting, etc. Developing the spatio-temporal point process and the related applications is the theme of this Special Issue, which treats an event as a point in the spatio-temporal space, with possibly extra attributes. The model captures the instantaneous happening rate of the events and their potential dependency. The derived use cases often refer to future events prediction, and causality estimation.

Automated summary

This article discusses two common forms of temporal data: synchronized temporal data and asynchronous event data. Previous approaches often convert event data into time series data, but it is more meaningful to directly establish models based on raw event data, especially for time-sensitive tasks. The theme of this article is the development of spatio-temporal point processes and their related applications, which treat an event as a point in the spatio-temporal space and capture the instantaneous happening rate of events and their potential dependency. Use cases include future event prediction and causality estimation.