{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-hawkes-processes-from-short-doubly","title":"Learning Hawkes Processes from Short Doubly-Censored Event Sequences","arxiv_id":"1702.07013","date":"2017-02-22","proceeding":"ICML 2017 8","authors":["Hongteng Xu","Dixin Luo","Hongyuan Zha"],"abstract":"Many real-world applications require robust algorithms to learn point\nprocesses based on a type of incomplete data --- the so-called short\ndoubly-censored (SDC) event sequences. We study this critical problem of\nquantitative asynchronous event sequence analysis under the framework of Hawkes\nprocesses by leveraging the idea of data synthesis. Given SDC event sequences\nobserved in a variety of time intervals, we propose a sampling-stitching data\nsynthesis method --- sampling predecessors and successors for each SDC event\nsequence from potential candidates and stitching them together to synthesize\nlong training sequences. The rationality and the feasibility of our method are\ndiscussed in terms of arguments based on likelihood. Experiments on both\nsynthetic and real-world data demonstrate that the proposed data synthesis\nmethod improves learning results indeed for both time-invariant and\ntime-varying Hawkes processes.","url_abs":"http://arxiv.org/abs/1702.07013v2","url_pdf":"http://arxiv.org/pdf/1702.07013v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-hawkes-processes-from-short-doubly","repo_url":"https://github.com/HongtengXu/Hawkes-Process-Toolkit","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"point-processes","task_name":"Point Processes"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}