ML//Time Series//Hawkes process

A self-exciting point process: each event temporarily raises the probability of the next, so events arrive in bursts.

A self-exciting point process: each event temporarily raises the probability of the next, so events arrive in bursts.

Intensity jumps after every event and decays back down; clusters emerge without any external trigger.

Models earthquake aftershocks, financial order flow, and neural event streams.

The observable proxy for its intensity is often an EWMA of the event stream.