Hawkes Processes
By Patrick J. Laub, Thomas Taimre, P. Pollett. 2015
166 citations recorded by the source corpus.
Hawkes processes are a particularly interesting class of stochastic process that have been applied in diverse areas, from earthquake modelling to financial analysis. They are point processes whose defining characteristic is that they 'self-excite', meaning that each arrival increases the rate of future arrivals for some period of time. Hawkes processes are well established, particularly within the financial literature, yet many of the treatments are inaccessible to one not acquainted with the topic. This survey provides background, introduces the field and historical developments, and touches upon all major aspects of Hawkes processes.
Subfields: Applied Mathematics
View Paper: https://www.semanticscholar.org/paper/282a2ba61a58afafad69ffe87a2e133f28642609
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In Applied Mathematics
Citations in the corpus
- Time2Vec: Learning a Vector Representation of Time (2019, background): “Asynchronous events can also be directly modeled with point processes ( e.g. , Poisson, Cox, and Hawkes point processes) [12, 31, 59, 34, 60] and continuous time normalizing flows [8].”
- Neural Jump Stochastic Differential Equations (2019, methodology): “We consider two widely used kernels: (1) the exponential kernel κ1, which is often used for its computational efficiency Laub et al. (2015); and (2) the power-law kernel κ2, which is used for modeling in seismology Ogata (1999) and social media Rizoiu et al.”