{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T14:38:44Z","timestamp":1785249524908,"version":"3.55.0"},"reference-count":43,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":["IEEE Signal Process. Lett."],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/lsp.2024.3438078","type":"journal-article","created":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T18:23:53Z","timestamp":1722882233000},"page":"2050-2054","source":"Crossref","is-referenced-by-count":13,"title":["Joint Selective State Space Model and Detrending for Robust Time Series Anomaly Detection"],"prefix":"10.1109","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8662-3783","authenticated-orcid":false,"given":"Junqi","family":"Chen","sequence":"first","affiliation":[{"name":"School of Marine Science and Technology, Northwestern Polytechnical University, Xi&#x0027;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6457-5407","authenticated-orcid":false,"given":"Xu","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Northwestern Polytechnical University, Xi&#x0027;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3674-1305","authenticated-orcid":false,"given":"Sylwan","family":"Rahardja","sequence":"additional","affiliation":[{"name":"School of Computing, University of Eastern Finland, Joensuu, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2521-2256","authenticated-orcid":false,"given":"Jiawei","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computing, University of Turku, Turku, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0831-6934","authenticated-orcid":false,"given":"Susanto","family":"Rahardja","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Northwestern Polytechnical University, Xi&#x0027;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Deep learning for time series anomaly detection: A survey","author":"Darban","year":"2022"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2020.3031004"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/rs14215394"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2022.3193903"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2021.3107750"},{"key":"ref6","first-page":"2021","article-title":"Revisiting time series outlier detection: Definitions and benchmarks","volume-title":"Proc. 35th Conf. Neural Inf. Process. Syst. Datasets Benchmarks Track (Round 1)","author":"Lai"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2023.3261138"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219845"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330672"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3128667"},{"key":"ref11","article-title":"Anomaly transformer: Time series anomaly detection with association discrepancy","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xu","year":"2021"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.14778\/3514061.3514067"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599295"},{"key":"ref14","first-page":"10758","article-title":"Drift doesnt matter: Dynamic decomposition with diffusion reconstruction for unstable multivariate time series anomaly detection","volume-title":"Proc. Int. Conf. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Wang","year":"2024"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1213\/4\/042050"},{"key":"ref16","article-title":"Mamba: Linear-time sequence modeling with selective state spaces","author":"Gu","year":"2023"},{"issue":"1","key":"ref17","first-page":"3","article-title":"STL: A seasonal-trend decomposition","volume":"6","author":"Cleveland","year":"1990","journal-title":"J. Official Statist."},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.2307\/2953682"},{"key":"ref19","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume-title":"Proc. Int. Conf. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Wu","year":"2021"},{"key":"ref20","first-page":"27268","article-title":"FedFormer: Frequency enhanced decomposed transformer for long-term series forecasting","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhou","year":"2022"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP48485.2024.10446482"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/BigData55660.2022.10021063"},{"key":"ref23","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai","year":"2018"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref26","first-page":"1310","article-title":"On the difficulty of training recurrent neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Pascanu","year":"2013"},{"key":"ref27","article-title":"Efficiently modeling long sequences with structured state spaces","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Gu","year":"2021"},{"key":"ref28","article-title":"VMAMBA: Visual state space model","author":"Liu","year":"2024"},{"key":"ref29","article-title":"Dual-path mamba: Short and long-term bidirectional selective structured state space models for speech separation","author":"Jiang","year":"2024"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2017.12.012"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335388"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8655(03)00003-5"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1162\/089976601750264965"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.17"},{"key":"ref35","first-page":"59","article-title":"Histogram-based outlier score (HBOS): A fast unsupervised anomaly detection algorithm","volume":"1","author":"Goldstein","year":"2012","journal-title":"KI-2012: Poster Demo Track"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-015-5521-0"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3159580"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CySWater.2016.7469060"},{"issue":"96","key":"ref39","first-page":"1","article-title":"Pyod: A python toolbox for scalable outlier detection","volume":"20","author":"Zhao","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098144"},{"key":"ref41","article-title":"Decoupled weight decay regularization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Loshchilov","year":"2019"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20680"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539339"}],"container-title":["IEEE Signal Processing Letters"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/97\/10380231\/10623192.pdf?arnumber=10623192","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T17:41:19Z","timestamp":1724089279000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10623192\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":43,"URL":"https:\/\/doi.org\/10.1109\/lsp.2024.3438078","relation":{},"ISSN":["1070-9908","1558-2361"],"issn-type":[{"value":"1070-9908","type":"print"},{"value":"1558-2361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}