{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T06:32:22Z","timestamp":1770273142477,"version":"3.49.0"},"reference-count":28,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T00:00:00Z","timestamp":1727136000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T00:00:00Z","timestamp":1727136000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,24]]},"DOI":"10.1109\/idsta62194.2024.10746986","type":"proceedings-article","created":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T18:36:28Z","timestamp":1731436588000},"page":"44-51","source":"Crossref","is-referenced-by-count":2,"title":["Predicting B2B Customer Churn using a Time Series Approach"],"prefix":"10.1109","author":[{"given":"Jim","family":"Ahlstrand","sequence":"first","affiliation":[{"name":"Telenor Sverige AB,Karlskrona,Sweden"}]},{"given":"Martin","family":"Boldt","sequence":"additional","affiliation":[{"name":"Blekinge Institute of Technology,Department of Computer Science,Karlskrona,Sweden"}]},{"given":"Anton","family":"Borg","sequence":"additional","affiliation":[{"name":"Blekinge Institute of Technology,Department of Computer Science,Karlskrona,Sweden"}]},{"given":"H\u00e5kan","family":"Grahn","sequence":"additional","affiliation":[{"name":"Blekinge Institute of Technology,Department of Computer Science,Karlskrona,Sweden"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/s0957-4174(02)00030-1"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.indmarman.2016.08.003"},{"key":"ref3","article-title":"Churn prediction with sequential data and deep neural networks. a comparative analysis","author":"Mena","journal-title":"arXiv 1909.11114, 2019. arXiv: 1909.11114"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113779"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-37309-2_15"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2023.036098"},{"issue":"7","key":"ref7","first-page":"486","article-title":"Understanding customer behaviour: A comprehensive survey of segmentation and classification techniques in the age of big data","volume":"11","author":"Awate","year":"2023","journal-title":"Int\u2019l J. of Intelligent Systems and Applications in Engineering"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1177\/23197145211062687"},{"key":"ref9","article-title":"Determinants of churn in telecommunication services: A systematic literature review","author":"Ribeiro","year":"2023","journal-title":"Management Review Quarterly"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-33742-1_30"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.indmarman.2021.10.001"},{"key":"ref12","article-title":"Telco customer churn"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-5243-4_12"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.indmarman.2014.06.016"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.24846\/v32i2y202303"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0191-6"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.29172\/7c2a6982-6d72-4cd8-bba6-2fccb06a7011"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/505282.505283"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511973000"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.2307\/2699986"},{"key":"ref21","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"Chung","year":"2014","journal-title":"arXiv: 1412. 3555[cs]"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1201.0490"},{"key":"ref23","article-title":"Automatic differentiation in PyTorch","author":"Paszke","year":"2017"},{"key":"ref24","volume-title":"Data Mining: Practical Machine Learning Tools and Techniques (The Morgan Kaufmann Series in Data Management Systems).","author":"Witten","year":"2016"},{"issue":"3","key":"ref25","doi-asserted-by":"crossref","DOI":"10.1145\/3494672","article-title":"A review on fairness in machine learning","volume":"55","author":"Pessach","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1002\/SERIES1345"},{"key":"ref27","doi-asserted-by":"crossref","volume-title":"Nonparametric Statistical Methods.","author":"Hollander","DOI":"10.1002\/9781119196037"},{"key":"ref28","article-title":"A unified approach to interpreting model predictions","volume-title":"Advances in Neural Information Processing Systems","volume":"30","author":"Lundberg"}],"event":{"name":"2024 Fifth International Conference on Intelligent Data Science Technologies and Applications (IDSTA)","location":"DUBROVNIK, Croatia","start":{"date-parts":[[2024,9,24]]},"end":{"date-parts":[[2024,9,27]]}},"container-title":["2024 Fifth International Conference on Intelligent Data Science Technologies and Applications (IDSTA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10746932\/10746934\/10746986.pdf?arnumber=10746986","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T14:46:05Z","timestamp":1732718765000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10746986\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,24]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1109\/idsta62194.2024.10746986","relation":{},"subject":[],"published":{"date-parts":[[2024,9,24]]}}}