Hawkes Models And Their Applications
About
The Hawkes process is a model for counting the number of arrivals to a system which exhibits the self-exciting property - that one arrival creates a heightened chance of further arrivals in the near future. The model, and its generalizations, have been applied in a plethora of disparate domains, though two particularly developed applications are in seismology and in finance. As the original model is elegantly simple, generalizations have been proposed which: track marks for each arrival, are multivariate, have a spatial component, are driven by renewal processes, treat time as discrete, and so on. This paper creates a cohesive review of the traditional Hawkes model and the modern generalizations, providing details on their construction, simulation algorithms, and giving key references to the appropriate literature for a detailed treatment.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Event Prediction | StackOverflow | RMSE1.341 | 42 | |
| Event Prediction | MIMIC | Accuracy79.5 | 15 | |
| Event Prediction | Neonate | Log-Likelihood-4.618 | 8 | |
| Event Prediction | Traffic | Log Loss-1.482 | 8 | |
| Event Prediction | BookOrder | LL-1.036 | 8 | |
| Event Prediction | Synthetic | LL-3.084 | 8 |