  The event sequence of many diverse systems is represented as a sequence of
discrete events in a continuous space. Examples of such an event sequence are
earthquake aftershock events, financial transactions, e-commerce transactions,
social network activity of a user, and the user's web search pattern. Finding
such an intricate pattern helps discover which event will occur in the future
and when it will occur. A Hawkes process is a mathematical tool used for
modeling such time series discrete events. Traditionally, the Hawkes process
uses a critical component for modeling data as an intensity function with a
parameterized kernel function. The Hawkes process's intensity function involves
two components: the background intensity and the effect of events' history.
However, such parameterized assumption can not capture future event
characteristics using past events data precisely due to bias in modeling kernel
function. This paper explores the recent advancement using novel deep
learning-based methods to model kernel function to remove such parametrized
kernel function. In the end, we will give potential future research directions
to improve modeling using the Hawkes process.
