{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T01:48:25Z","timestamp":1772934505806,"version":"3.50.1"},"reference-count":40,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"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":[[2025,12,8]]},"DOI":"10.1109\/bigdata66926.2025.11402523","type":"proceedings-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T20:57:57Z","timestamp":1772830677000},"page":"1555-1562","source":"Crossref","is-referenced-by-count":0,"title":["MAD: Multimodal Framework for Adaptive Time-Series Anomaly Detection"],"prefix":"10.1109","author":[{"given":"Jeongeum","family":"Seok","sequence":"first","affiliation":[{"name":"POSTECH,Department of Computer Science and Engineering,Pohang,South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wook-Shin","family":"Han","sequence":"additional","affiliation":[{"name":"Graduate School of Artificial Intelligence, POSTECH,Pohang,South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2886457"},{"key":"ref3","first-page":"108 231","article-title":"The elephant in the room: Towards a reliable time-series anomaly detection benchmark","volume-title":"Advances in Neural Information Processing Systems","volume":"37","author":"Liu","year":"2024"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2379776.2379788"},{"key":"ref5","first-page":"454","article-title":"Learning comprehensible descriptions of multivariate time series","volume-title":"Proceedings of the Sixteenth International Conference on Machine Learning","author":"Kadous","year":"1999"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.14778\/3551793.3551830"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3317791"},{"key":"ref8","volume-title":"Tspulse: Dual space tiny pre-trained models for rapid time-series analysis","author":"Ekambaram","year":"2025"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219845"},{"key":"ref10","volume-title":"Dynamic Programming, ser. Rand Corporation research study","author":"Bellman","year":"1957"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.14778\/3538598.3538602"},{"issue":"2","key":"ref12","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/14786440109462720","article-title":"Principal components analysis","volume":"6","author":"Pearson","year":"1901","journal-title":"The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science"},{"key":"ref13","volume-title":"Principal Component Analysis, ser. Springer Series in Statistics","author":"Jolliffe","year":"2002"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02491"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"105002","DOI":"10.1016\/j.ebiom.2024.105002","article-title":"(predictable) performance bias in unsupervised anomaly detection","volume":"101","author":"Meissen","year":"2024","journal-title":"eBioMedicine"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.abq6147"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CySWater.2016.7469060"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1161\/01.cir.101.23.e215"},{"key":"ref19","volume-title":"Controlled anomalies time series (cats) dataset","author":"Fleith","year":"2023"},{"key":"ref20","first-page":"207","volume-title":"Which Outlier Detection Algorithm Should I Use?","author":"Aggarwal","year":"2017"},{"issue":"7","key":"ref21","first-page":"7194","article-title":"Towards a rigorous evaluation of time-series anomaly detection","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"36","author":"Kim","year":"2022"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.5220\/0006639801080116"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2009.2036165"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.14778\/3476249.3476307"},{"key":"ref25","volume-title":"Gecco industrial challenge 2018 dataset: A water quality dataset for the \u2019internet of things: Online anomaly detection for drinking water quality\u2019 competition at the genetic and evolutionary computation conference 2018","author":"Moritz"},{"key":"ref26","volume-title":"Multivariate industrial time series with cyber-attack simulation: Fault detection using an lstm-based predictive data model","author":"Filonov","year":"2016"},{"key":"ref27","first-page":"55","volume-title":"Anomaly Detection and Localization for Cyber-Physical Production Systems with Self-Organizing Maps","author":"von Birgelen","year":"2018"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/INSS.2010.5573462"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467174"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330672"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CIC.1990.144257"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1029\/97JC02906"},{"key":"ref33","volume-title":"Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities","year":"2025"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.14778\/3514061.3514067"},{"key":"ref35","article-title":"Timesnet: Temporal 2d-variation modeling for general time series analysis","volume-title":"The Eleventh International Conference on Learning Representations","author":"Wu","year":"2023"},{"key":"ref36","article-title":"Anomaly transformer: Time series anomaly detection with association discrepancy","volume-title":"International Conference on Learning Representations","author":"Xu","year":"2022"},{"key":"ref37","volume-title":"xlstmad: A powerful xlstm-based method for anomaly detection","author":"Faber","year":"2025"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3709663"},{"issue":"1","key":"ref39","first-page":"3","article-title":"Stl: A seasonaltrend decomposition procedure based on loess","volume":"6","author":"Cleveland","year":"1990","journal-title":"Journal of Official Statistics"},{"issue":"2","key":"ref40","doi-asserted-by":"crossref","first-page":"221","DOI":"10.2307\/1268354","article-title":"On the detection of many outliers","volume":"17","author":"Rosner","year":"1975","journal-title":"Technometrics"}],"event":{"name":"2025 IEEE International Conference on Big Data (BigData)","location":"Macau, China","start":{"date-parts":[[2025,12,8]]},"end":{"date-parts":[[2025,12,11]]}},"container-title":["2025 IEEE International Conference on Big Data (BigData)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11400704\/11400712\/11402523.pdf?arnumber=11402523","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T07:09:44Z","timestamp":1772867384000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11402523\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,8]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/bigdata66926.2025.11402523","relation":{},"subject":[],"published":{"date-parts":[[2025,12,8]]}}}