{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T17:17:31Z","timestamp":1777051051681,"version":"3.51.4"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030864859","type":"print"},{"value":"9783030864866","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-86486-6_18","type":"book-chapter","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T15:25:48Z","timestamp":1631201148000},"page":"289-304","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deviation-Based Marked Temporal Point Process for Marker Prediction"],"prefix":"10.1007","author":[{"given":"Anand Vir Singh","family":"Chauhan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shivshankar","family":"Reddy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maneet","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karamjit","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tanmoy","family":"Bhowmik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"18_CR1","unstructured":"Daley, D.J., Vere-Jones, D.: An Introduction to the Theory of Point Processes: Volume I: Elementary Theory and Methods. Springer, Heidelberg (2003)"},{"key":"18_CR2","doi-asserted-by":"crossref","unstructured":"Diggle, P.J.: Statistical Analysis of Spatial and Spatio-Temporal Point Patterns. CRC Press, Boca Raton (2013)","DOI":"10.1201\/b15326"},{"key":"18_CR3","doi-asserted-by":"crossref","unstructured":"Du, N., Dai, H., Trivedi, R., Upadhyay, U., Gomez-Rodriguez, M., Song, L.: Recurrent marked temporal point processes: embedding event history to vector. In: Proceedings of ACM KDD, pp. 1555\u20131564 (2016)","DOI":"10.1145\/2939672.2939875"},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Grant, S., Betts, B.: Encouraging user behaviour with achievements: an empirical study. In: Proceedings of MSR, pp. 65\u201368 (2013)","DOI":"10.1109\/MSR.2013.6624007"},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"Guo, R., Li, J., Liu, H.: Initiator: noise-contrastive estimation for marked temporal point process. In: Proceedings of IJCAI, pp. 2191\u20132197 (2018)","DOI":"10.24963\/ijcai.2018\/303"},{"issue":"1","key":"18_CR6","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1093\/biomet\/58.1.83","volume":"58","author":"AG Hawkes","year":"1971","unstructured":"Hawkes, A.G.: Spectra of some self-exciting and mutually exciting point processes. Biometrika 58(1), 83\u201390 (1971)","journal-title":"Biometrika"},{"key":"18_CR7","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)"},{"issue":"8","key":"18_CR8","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"issue":"3","key":"18_CR9","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/0304-4149(79)90008-5","volume":"8","author":"V Isham","year":"1979","unstructured":"Isham, V., Westcott, M.: A self-correcting point process. Stoch. Process. Appl. 8(3), 335\u2013347 (1979)","journal-title":"Stoch. Process. Appl."},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"Ji, Y., et al.: Temporal heterogeneous interaction graph embedding for next-item recommendation. In: Proceedings of ECML-PKDD (2020)","DOI":"10.1007\/978-3-030-67664-3_19"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Kemp, A.: Poisson Processes. Wiley Online Library (1994)","DOI":"10.1112\/blms\/26.6.612"},{"key":"18_CR12","doi-asserted-by":"crossref","unstructured":"Keogh, E., Chu, S., Hart, D., Pazzani, M.: Segmenting time series: a survey and novel approach. In: Data Mining in Time Series Databases, pp. 1\u201321 (2004)","DOI":"10.1142\/9789812565402_0001"},{"key":"18_CR13","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"2","key":"18_CR14","doi-asserted-by":"publisher","first-page":"143","DOI":"10.11613\/BM.2013.018","volume":"23","author":"ML McHugh","year":"2013","unstructured":"McHugh, M.L.: The chi-square test of independence. Biochem. Med. 23(2), 143\u2013149 (2013)","journal-title":"Biochem. Med."},{"key":"18_CR15","unstructured":"Mei, H., Eisner, J.: The neural hawkes process: A neurally self-modulating multivariate point process. arXiv preprint arXiv:1612.09328 (2016)"},{"key":"18_CR16","unstructured":"Pan, Z., Du, H., Ngiam, K.Y., Wang, F., Shum, P., Feng, M.: A self-correcting deep learning approach to predict acute conditions in critical care. arXiv preprint arXiv:1901.04364 (2019)"},{"key":"18_CR17","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Proceedings of NeurIPS, pp. 8024\u20138035 (2019)"},{"key":"18_CR18","unstructured":"Rasmussen, J.G.: Temporal Point Processes: The Conditional Intensity Function. Lecture Notes, Jan (2011)"},{"key":"18_CR19","unstructured":"Shchur, O., Bilo\u0161, M., G\u00fcnnemann, S.: Intensity-free learning of temporal point processes. arXiv preprint arXiv:1909.12127 (2019)"},{"key":"18_CR20","doi-asserted-by":"crossref","unstructured":"T\u00fcrkmen, A.C., Wang, Y., Smola, A.J.: Fastpoint: scalable deep point processes. In: Proceedings of ECML-PKDD, pp. 465\u2013480 (2019)","DOI":"10.1007\/978-3-030-46147-8_28"},{"issue":"4","key":"18_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3331449","volume":"10","author":"I Verenich","year":"2019","unstructured":"Verenich, I., Dumas, M., Rosa, M.L., Maggi, F.M., Teinemaa, I.: Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring. ACM TIST 10(4), 1\u201334 (2019)","journal-title":"ACM TIST"},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Wang, Y., Liu, S., Shen, H., Gao, J., Cheng, X.: Marked temporal dynamics modeling based on recurrent neural network. In: Proceedings of PAKDD, pp. 786\u2013798 (2017)","DOI":"10.1007\/978-3-319-57454-7_61"},{"key":"18_CR23","doi-asserted-by":"crossref","unstructured":"Wu, W., Yan, J., Yang, X., Zha, H.: Decoupled learning for factorial marked temporal point processes. In: Proceedings of ACM KDD, pp. 2516\u20132525 (2018)","DOI":"10.1145\/3219819.3220035"},{"key":"18_CR24","doi-asserted-by":"crossref","unstructured":"Xiao, S., Yan, J., Yang, X., Zha, H., Chu, S.: Modeling the intensity function of point process via recurrent neural networks. In: Proceedings of AAAI, vol. 31 (2017)","DOI":"10.1609\/aaai.v31i1.10724"},{"key":"18_CR25","unstructured":"Zhang, Q., Lipani, A., Kirnap, O., Yilmaz, E.: Self-attentive hawkes processes. arXiv preprint arXiv:1907.07561 (2019)"},{"key":"18_CR26","unstructured":"Zhang, Q., Lipani, A., Kirnap, O., Yilmaz, E.: Self-attentive hawkes process. In: Proceedings of ICML, pp. 11183\u201311193 (2020)"},{"key":"18_CR27","doi-asserted-by":"crossref","unstructured":"Zhao, L.: Event Prediction in Big Data Era: A Systematic Survey. arXiv preprint arXiv:2007.09815 (2020)","DOI":"10.36227\/techrxiv.12733049"},{"key":"18_CR28","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Erdogdu, M.A., He, H.Y., Rajaraman, A., Leskovec, J.: Seismic: a self-exciting point process model for predicting tweet popularity. In: Proceedings of KDD, pp. 1513\u20131522 (2015)","DOI":"10.1145\/2783258.2783401"},{"key":"18_CR29","unstructured":"Zuo, S., Jiang, H., Li, Z., Zhao, T., Zha, H.: Transformer hawkes process. In: Proceedings of ICML, pp. 11692\u201311702 (2020)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86486-6_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T22:07:23Z","timestamp":1757369243000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86486-6_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030864859","9783030864866"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86486-6_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"10 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bilbao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2021.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"869","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"210","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-9","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held online due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}