Transformer Hawkes Process

Event sequence data are naturally observed in our daily life. Through social media such as Twitter and Facebook, we share our experiences and respond to other users information (Yang et al., 2011). In these websites, each user has a sequence of events such as tweets and interactions. Hundreds of millions of users generate large amounts of tweets, which are essentially sequences of events at different time stamps. Besides social media, event data also exist in domains like financial transactions (Bacry et al., 2015) and personalized healthcare (Wang et al., 2018). For example, in electronic medical records, tests and diagnoses of each patient can be treated as a sequence of events. Unlike other sequential data such as time series, event sequences tend to be asynchronous (Ross et al., 1996), which means time intervals between events are just as important as the order of them to describe their dynamics. Also, depending on specific application requirements, event data show sophisticated dependencies on their history. Point process is a powerful tool for modeling sequences of discrete events in continuous time, and the technique has been widely applied.