# Benefits from Superposed Hawkes Processes

arXiv: https://arxiv.org/abs/1710.05115 · HTML: https://mlsanity.com/paper/1710.05115/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2017.

## Built on by (most cited first, 5 of 5)

- [FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels](https://mlsanity.com/paper/2210.04635/) (2022)
- [Bayesian Neural Hawkes Process for Event Uncertainty Prediction](https://mlsanity.com/paper/2112.14474/) (2021)
- [Universal Multi-Modal Traceformer: Integrating Heterogeneous Context for Process Event Prediction](https://mlsanity.com/paper/2609.24579/) (2026)
- [Kronecker-Structured Nonparametric Spatiotemporal Point Processes](https://mlsanity.com/paper/2603.23746/) (2026)
- [Structured Neural Marked Point Processes for Interpretable Event Interaction Modeling](https://mlsanity.com/paper/2605.17568/) (2026)

## Abstract (arXiv)

The superposition of temporal point processes has been studied for many years, although the usefulness of such models for practical applications has not be fully developed. We investigate superposed Hawkes process as an important class of such models, with properties studied in the framework of least squares estimation. The superposition of Hawkes processes is demonstrated to be beneficial for tightening the upper bound of excess risk under certain conditions, and we show the feasibility of the benefit in typical situations. The usefulness of superposed Hawkes processes is verified on synthetic data, and its potential to solve the cold-start problem of recommendation systems is demonstrated on real-world data.