Incremental recommendation systems have garnered significant research interest since they
ideally adapt to users' ongoing events (such as clicking, browsing, and reviewing) and …

Forecasting online product sales is essential for retailers and e-commerce platforms, but it
can be challenging owing to the complex dynamics and mixed trends in sales data. Popular …

The expressive ability of classic Hawkes processes is limited due to the parametric
assumption on the baseline intensity and triggering kernel. Therefore, it is desirable to …

Many applications involve multi-type event data. Understanding the complex influences of
the events on each other is critical to discover useful knowledge and to predict future events …

Temporal point processes (TPPs) are stochastic process models used to characterize event
sequences occurring in continuous time. Traditional statistical TPPs have a long-standing …

The spatio-temporal epidemic type aftershock sequence (ETAS) model is widely used to
describe the self-exciting nature of earthquake occurrences. While traditional inference …

Event data consisting of time of occurrence of the events arises in several real-world
applications. A commonly used framework to model such events is known as temporal point …

A Neyman-Scott process is a special case of a Cox process. The latent and observable
stochastic processes are both Poisson processes. We consider a deep Neyman-Scott …

Interval-censored data solely records the aggregated counts of events during specific time
intervals-such as the number of patients admitted to the hospital or the volume of vehicles …

Traditionally, Hawkes processes are used to model time-continuous point processes with
history dependence. Here, we propose an extended model where the self-effects are of both …