{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T04:20:58Z","timestamp":1679372458751},"reference-count":15,"publisher":"MIT Press","issue":"4","content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,3,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>When applying a point process to a real-world problem, an appropriate intensity function model should be designed based on physical and mathematical prior knowledge. Recently, a fully trainable deep learning\u2013based approach has been developed for temporal point processes. In this approach, a cumulative hazard function (CHF) capable of systematic computation of adaptive intensity function is modeled in a data-driven manner. However, in this approach, although many applications of point processes generate various kinds of information such as location, magnitude, and depth, the mark information of events is not considered. To overcome this limitation, we propose a fully trainable marked point process method for modeling decomposed CHFs for time and mark prediction using multistream deep neural networks. We demonstrate the effectiveness of the proposed method through experiments with synthetic and real-world event data.<\/jats:p>","DOI":"10.1162\/neco_a_01572","type":"journal-article","created":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T20:25:44Z","timestamp":1677270344000},"page":"699-726","update-policy":"http:\/\/dx.doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":0,"title":["Multistream-Based Marked Point Process With Decomposed Cumulative Hazard Functions"],"prefix":"10.1162","volume":"35","author":[{"given":"Hirotaka","family":"Hachiya","sequence":"first","affiliation":[{"name":"Graduate School of System Engineering, Wakayama University, Sakaedani 930, Wakayama-city, Wakayama 640-8510, Japan hhachiya@wakayama-u.ac.jp"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sujun","family":"Hong","sequence":"additional","affiliation":[{"name":"Graduate School of System Engineering, Wakayama University, Sakaedani 930, Wakayama-city, Wakayama 640-8510, Japan hong.sujun@g.wakayama-u.jp"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","published-online":{"date-parts":[[2023,3,18]]},"reference":[{"key":"2023032023153701000_","article-title":"Neural spatio-temporal point processes","volume-title":"Proceedings of International Conference on Learning Representations","author":"Chen","year":"2021"},{"key":"2023032023153701000_","first-page":"1555","article-title":"Recurrent marked temporal point processes: Embedding event history to vector","volume-title":"Proceedings of International Conference on Knowledge Discovery and Data Mining","author":"Du","year":"2016"},{"key":"2023032023153701000_","first-page":"6538","article-title":"Recurrent Poisson process unit for speech recognition","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Huang","year":"2019"},{"key":"2023032023153701000_","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v088.c01","article-title":"ETAS: An R package for fitting the space-time ETAS model to earthquake data","volume":"88","author":"Jalilian","year":"2019","journal-title":"Journal of Statistical Software"},{"key":"2023032023153701000_","article-title":"Learning temporal point processes via reinforcement learning","volume-title":"Advances in neural information processing systems","author":"Li","year":"2018"},{"key":"2023032023153701000_","article-title":"Citi bike system data: Citi bike NYC","author":"Lyft","year":"2022"},{"key":"2023032023153701000_","article-title":"The neural Hawkes process: A neurally self-modulating multivariate point process","volume-title":"Advances in neural information processing systems","author":"Mei","year":"2017"},{"key":"2023032023153701000_","article-title":"Northern California earthquake catalog and phase data","author":"NCEDC","year":"2014"},{"key":"2023032023153701000_","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1023\/A:1003403601725","article-title":"Space-time point-process models for earthquake occurrences","volume":"50","author":"Ogata","year":"1998","journal-title":"Annals of the Institute of Statistical Mathematics"},{"key":"2023032023153701000_","first-page":"2122","article-title":"Fully neural network based model for general temporal point processes","author":"Omi","year":"2019"},{"key":"2023032023153701000_","article-title":"Police Department incident reports: Historical 2003 to May 2018. City and County of San Francisco","author":"San Francisco","year":"2021"},{"key":"2023032023153701000_","article-title":"Deep reinforcement learning of marked temporal point processes","volume-title":"Advances in neural information processing systems","author":"Upadhyay","year":"2018"},{"key":"2023032023153701000_","first-page":"1597","article-title":"Modeling the intensity function of point process via recurrent neural networks","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Xiao","year":"2017"},{"key":"2023032023153701000_","author":"Zhu","year":"2019","journal-title":"Imitation learning of neural spatiotemporal point processes."},{"key":"2023032023153701000_","first-page":"11692","article-title":"Transformer Hawkes process","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Zuo","year":"2020"}],"container-title":["Neural Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/direct.mit.edu\/neco\/article-pdf\/35\/4\/699\/2075318\/neco_a_01572.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/direct.mit.edu\/neco\/article-pdf\/35\/4\/699\/2075318\/neco_a_01572.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T23:15:55Z","timestamp":1679354155000},"score":1,"resource":{"primary":{"URL":"https:\/\/direct.mit.edu\/neco\/article\/35\/4\/699\/114873\/Multistream-Based-Marked-Point-Process-With"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,18]]},"references-count":15,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,3,18]]},"published-print":{"date-parts":[[2023,3,18]]}},"URL":"https:\/\/doi.org\/10.1162\/neco_a_01572","relation":{},"ISSN":["0899-7667","1530-888X"],"issn-type":[{"value":"0899-7667","type":"print"},{"value":"1530-888X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,4]]},"published":{"date-parts":[[2023,3,18]]}}}