{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T15:28:24Z","timestamp":1787239704695,"version":"build-2736575974"},"reference-count":107,"publisher":"Society for Industrial & Applied Mathematics (SIAM)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["SIAM Rev."],"published-print":{"date-parts":[[2023,5]]},"abstract":"<jats:p>Hawkes processes are a type of point process that models self-excitement among time events. They have been used in a myriad of applications, ranging from finance and earthquakes to crime rates and social network activity analysis. Recently, a variety of different tools and algorithms have been presented at top-tier machine learning conferences. This work aims to give a broad view of recent advances in Hawkes process modeling and inference suitable for a newcomer to the field. The parametric, nonparametric, deep learning, and reinforcement learning approaches are broadly discussed, along with the current research challenges for the topic and the real-world limitations of each approach. Illustrative application examples in the modeling of retweeting behavior, earthquake aftershock occurrence, and malaria outbreak modeling are also briefly discussed.<\/jats:p>","DOI":"10.1137\/21m1396927","type":"journal-article","created":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T10:05:24Z","timestamp":1683540324000},"page":"331-374","source":"Crossref","is-referenced-by-count":19,"title":["Hawkes Processes Modeling, Inference, and Control: An Overview"],"prefix":"10.1137","volume":"65","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1976-5719","authenticated-orcid":true,"given":"Rafael","family":"Lima","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"351","published-online":{"date-parts":[[2023,5,9]]},"reference":[{"key":"atypb1","doi-asserted-by":"publisher","DOI":"10.1137\/15M1011469"},{"key":"atypb2","first-page":"1","volume":"18","author":"Achab M.","year":"2017","journal-title":"J. Mach. Learn. 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