{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T19:22:59Z","timestamp":1775503379288,"version":"3.50.1"},"reference-count":70,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T00:00:00Z","timestamp":1768003200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002920","name":"University Grants Committee Research Grants Council","doi-asserted-by":"publisher","award":["R6003-21"],"award-info":[{"award-number":["R6003-21"]}],"id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007156","name":"Innovation and Technology Commission - Hong Kong","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007156","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003452","name":"Innovation and Technology Commission","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003452","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010428","name":"Innovation and Technology Fund","doi-asserted-by":"publisher","award":["ITS\/004\/21FP"],"award-info":[{"award-number":["ITS\/004\/21FP"]}],"id":[{"id":"10.13039\/501100010428","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neural Networks"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.neunet.2026.108568","type":"journal-article","created":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T16:03:17Z","timestamp":1768060997000},"page":"108568","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["An online forecasting-based fine-tuning pipeline for time-series anomaly prediction"],"prefix":"10.1016","volume":"198","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6490-8945","authenticated-orcid":false,"given":"Zhou","family":"Zhou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5978-5590","authenticated-orcid":false,"given":"Van Hoan","family":"Trinh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0403-450X","authenticated-orcid":false,"given":"Yuet Ming Joyce","family":"Yue","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3716-8125","authenticated-orcid":false,"given":"Dit-Yan","family":"Yeung","sequence":"additional","affiliation":[]},{"given":"Ka-Hing","family":"Wong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8724-0696","authenticated-orcid":false,"given":"Wai-Kin","family":"Wong","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neunet.2026.108568_bib0001","series-title":"Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining","first-page":"2485","article-title":"Practical approach to asynchronous multivariate time series anomaly detection and localization","author":"Abdulaal","year":"2021"},{"key":"10.1016\/j.neunet.2026.108568_bib0002","series-title":"An introduction to outlier analysis","author":"Aggarwal","year":"2017"},{"key":"10.1016\/j.neunet.2026.108568_bib0003","series-title":"Conference on learning theory","first-page":"172","article-title":"Online learning for time series prediction","author":"Anava","year":"2013"},{"key":"10.1016\/j.neunet.2026.108568_bib0004","first-page":"7982","article-title":"Dynamic local regret for non-convex online forecasting","volume":"32","author":"Aydore","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"3","key":"10.1016\/j.neunet.2026.108568_bib0005","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3444690","article-title":"A review on outlier\/anomaly detection in time series data","volume":"54","author":"Bl\u00e1zquez-Garc\u00eda","year":"2021","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"10.1016\/j.neunet.2026.108568_bib0006","series-title":"Proceedings of the 2000\u202fACM SIGMOD international conference on management of data","first-page":"93","article-title":"Lof: Identifying density-based local outliers","author":"Breunig","year":"2000"},{"key":"10.1016\/j.neunet.2026.108568_bib0007","series-title":"2020 5th International conference on computer science and engineering (UBMK)","first-page":"94","article-title":"Anomaly detection with multivariate k-sigma score using monte carlo","author":"\u00c7etin","year":"2020"},{"issue":"5","key":"10.1016\/j.neunet.2026.108568_bib0008","first-page":"6074","article-title":"Semisupervised anomaly detection of multivariate time series based on a variational autoencoder","volume":"53","author":"Chen","year":"2023","journal-title":"Applied Intelligence"},{"key":"10.1016\/j.neunet.2026.108568_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.101819","article-title":"Long sequence time-series forecasting with deep learning: A survey","volume":"97","author":"Chen","year":"2023","journal-title":"Information Fusion"},{"key":"10.1016\/j.neunet.2026.108568_bib0010","series-title":"Proceedings of the conference on research in adaptive and convergent systems","first-page":"161","article-title":"Outlier detection using isolation forest and local outlier factor","author":"Cheng","year":"2019"},{"key":"10.1016\/j.neunet.2026.108568_bib0011","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"4027","article-title":"Graph neural network-based anomaly detection in multivariate time series","volume":"vol. 35","author":"Deng","year":"2021"},{"issue":"4","key":"10.1016\/j.neunet.2026.108568_bib0012","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1049\/iet-rpg.2019.0957","article-title":"Fault detection of photovoltaic array based on grubbs criterion and local outlier factor","volume":"14","author":"Ding","year":"2020","journal-title":"IET Renewable Power Generation"},{"issue":"4","key":"10.1016\/j.neunet.2026.108568_bib0013","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2523813","article-title":"A survey on concept drift adaptation","volume":"46","author":"Gama","year":"2014","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"10.1016\/j.neunet.2026.108568_bib0014","series-title":"2020 IEEE International conference on big data (big data)","first-page":"33","article-title":"Tadgan: Time series anomaly detection using generative adversarial networks","author":"Geiger","year":"2020"},{"key":"10.1016\/j.neunet.2026.108568_bib0015","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 and Demo Track"},{"issue":"5","key":"10.1016\/j.neunet.2026.108568_bib0016","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1109\/TSP.2018.2889982","article-title":"Online forecasting matrix factorization","volume":"67","author":"Gultekin","year":"2018","journal-title":"IEEE Transactions on Signal Processing"},{"issue":"4","key":"10.1016\/j.neunet.2026.108568_bib0017","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1109\/TKDE.2019.2947676","article-title":"Extended isolation forest","volume":"33","author":"Hariri","year":"2019","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.neunet.2026.108568_bib0018","series-title":"International conference on machine learning","first-page":"1433","article-title":"Efficient regret minimization in non-convex games","author":"Hazan","year":"2017"},{"issue":"6","key":"10.1016\/j.neunet.2026.108568_bib0019","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.14778\/3583140.3583155","article-title":"OneshotSTL: One-shot seasonal-trend decomposition for online time series anomaly detection and forecasting","volume":"16","author":"He","year":"2023","journal-title":"Proceedings of the VLDB Endowment"},{"issue":"1","key":"10.1016\/j.neunet.2026.108568_bib0020","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/TBDATA.2017.2711039","article-title":"Time series anomaly detection for trustworthy services in cloud computing systems","volume":"8","author":"Huang","year":"2017","journal-title":"IEEE Transactions on Big Data"},{"issue":"3","key":"10.1016\/j.neunet.2026.108568_bib0021","doi-asserted-by":"crossref","first-page":"1604","DOI":"10.1109\/TNSE.2022.3148276","article-title":"Timeautoad: Autonomous anomaly detection with self-supervised contrastive loss for multivariate time series","volume":"9","author":"Jiao","year":"2022","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"10.1016\/j.neunet.2026.108568_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.105964","article-title":"Time-series anomaly detection with stacked transformer representations and 1d convolutional network","volume":"120","author":"Kim","year":"2023","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.neunet.2026.108568_bib0023","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"16060","article-title":"Tods: An automated time series outlier detection system","volume":"vol. 35","author":"Lai","year":"2021"},{"issue":"2","key":"10.1016\/j.neunet.2026.108568_bib0024","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.cja.2022.05.001","article-title":"A method for satellite time series anomaly detection based on fast-DTW and improved-KNN","volume":"36","author":"Langfu","year":"2023","journal-title":"Chinese Journal of Aeronautics"},{"key":"10.1016\/j.neunet.2026.108568_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.125575","article-title":"A dynamic anomaly detection method of building energy consumption based on data mining technology","volume":"263","author":"Lei","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.neunet.2026.108568_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.111002","article-title":"A novel unsupervised framework for time series data anomaly detection via spectrum decomposition","volume":"280","author":"Lei","year":"2023","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.neunet.2026.108568_bib0027","series-title":"International conference on artificial neural networks","first-page":"703","article-title":"Mad-gan: Multivariate anomaly detection for time series data with generative adversarial networks","author":"Li","year":"2019"},{"issue":"1","key":"10.1016\/j.neunet.2026.108568_bib0028","first-page":"1","article-title":"Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution","volume":"17","author":"Li","year":"2023","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"10.1016\/j.neunet.2026.108568_bib0029","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.inffus.2022.10.008","article-title":"Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges","volume":"91","author":"Li","year":"2023","journal-title":"Information Fusion"},{"issue":"3","key":"10.1016\/j.neunet.2026.108568_bib0030","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1109\/TNNLS.2020.2980749","article-title":"Anomaly detection of time series with smoothness-inducing sequential variational auto-encoder","volume":"32","author":"Li","year":"2020","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.neunet.2026.108568_bib0031","series-title":"2020\u202fIEEE International conference on data mining (ICDM)","first-page":"1118","article-title":"Copod: Copula-based outlier detection","author":"Li","year":"2020"},{"issue":"12","key":"10.1016\/j.neunet.2026.108568_bib0032","doi-asserted-by":"crossref","first-page":"12181","DOI":"10.1109\/TKDE.2022.3159580","article-title":"Ecod: Unsupervised outlier detection using empirical cumulative distribution functions","volume":"35","author":"Li","year":"2022","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"1","key":"10.1016\/j.neunet.2026.108568_bib0033","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1109\/TETCI.2023.3290027","article-title":"HybridAD: A hybrid model-driven anomaly detection approach for multivariate time series","volume":"8","author":"Lin","year":"2023","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"key":"10.1016\/j.neunet.2026.108568_bib0034","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1007\/s10618-020-00685-w","article-title":"Matrix profile goes MAD: Variable-length motif and discord discovery in data series","volume":"34","author":"Linardi","year":"2020","journal-title":"Data Mining and Knowledge Discovery"},{"key":"10.1016\/j.neunet.2026.108568_bib0035","series-title":"Proceedings of the AAAI conference on artificial intelligence","article-title":"Online arima algorithms for time series prediction","volume":"vol. 30","author":"Liu","year":"2016"},{"key":"10.1016\/j.neunet.2026.108568_bib0036","series-title":"2008 Eighth IEEE international conference on data mining","first-page":"413","article-title":"Isolation forest","author":"Liu","year":"2008"},{"issue":"11","key":"10.1016\/j.neunet.2026.108568_bib0037","doi-asserted-by":"crossref","first-page":"4364","DOI":"10.14778\/3749646.3749699","article-title":"Tsb-autoad: Towards automated solutions for time-series anomaly detection","volume":"18","author":"Liu","year":"2025","journal-title":"Proceedings of the VLDB Endowment"},{"issue":"2","key":"10.1016\/j.neunet.2026.108568_bib0038","doi-asserted-by":"crossref","first-page":"774","DOI":"10.3390\/app14020774","article-title":"Unsupervised deep anomaly detection for industrial multivariate time series data","volume":"14","author":"Liu","year":"2024","journal-title":"Applied Sciences"},{"issue":"1","key":"10.1016\/j.neunet.2026.108568_bib0039","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1111\/joes.12429","article-title":"Machine learning advances for time series forecasting","volume":"37","author":"Masini","year":"2023","journal-title":"Journal of Economic Surveys"},{"issue":"1","key":"10.1016\/j.neunet.2026.108568_bib0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2023.103569","article-title":"Reconstruction-based anomaly detection for multivariate time series using contrastive generative adversarial networks","volume":"61","author":"Miao","year":"2024","journal-title":"Information Processing & Management"},{"issue":"1","key":"10.1016\/j.neunet.2026.108568_bib0041","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3531326","article-title":"Time series prediction using deep learning methods in healthcare","volume":"14","author":"Morid","year":"2023","journal-title":"ACM Transactions on Management Information Systems"},{"key":"10.1016\/j.neunet.2026.108568_bib0042","series-title":"International conference on learning representations","article-title":"A time series is worth 64 words: Long-term forecasting with transformers","author":"Nie","year":"2023"},{"key":"10.1016\/j.neunet.2026.108568_bib0043","series-title":"Proceedings of the 31st ACM SIGKDD conference on knowledge discovery and data mining v. 2","first-page":"6151","article-title":"Advances in time-series anomaly detection: Algorithms, benchmarks, and evaluation measures","author":"Paparrizos","year":"2025"},{"issue":"11","key":"10.1016\/j.neunet.2026.108568_bib0044","doi-asserted-by":"crossref","first-page":"2774","DOI":"10.14778\/3551793.3551830","article-title":"Volume under the surface: A new accuracy evaluation measure for time-series anomaly detection","volume":"15","author":"Paparrizos","year":"2022","journal-title":"Proceedings of the VLDB Endowment"},{"key":"10.1016\/j.neunet.2026.108568_bib0045","article-title":"Learning fast and slow for online time series forecasting","author":"Pham","year":"2023","journal-title":"The International Conference on Learning Representations"},{"issue":"1","key":"10.1016\/j.neunet.2026.108568_bib0046","doi-asserted-by":"crossref","first-page":"1268","DOI":"10.1007\/s11227-023-05534-3","article-title":"An efficient GAN-based predictive framework for multivariate time series anomaly prediction in cloud data centers","volume":"80","author":"Qi","year":"2024","journal-title":"The Journal of Supercomputing"},{"key":"10.1016\/j.neunet.2026.108568_bib0047","series-title":"2017\u202fIEEE Security and privacy workshops (SPW)","first-page":"229","article-title":"Using gaussian mixture models to detect outliers in seasonal univariate network traffic","author":"Reddy","year":"2017"},{"key":"10.1016\/j.neunet.2026.108568_bib0048","series-title":"Proceedings of the twenty-seventh international joint conference on artificial intelligence, IJCAI-18","first-page":"2660","article-title":"Online deep learning: Learning deep neural networks on the fly","author":"Sahoo","year":"2018"},{"issue":"9","key":"10.1016\/j.neunet.2026.108568_bib0049","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.14778\/3538598.3538602","article-title":"Anomaly detection in time series: A comprehensive evaluation","volume":"15","author":"Schmidl","year":"2022","journal-title":"Proceedings of the VLDB Endowment"},{"key":"10.1016\/j.neunet.2026.108568_bib0050","first-page":"582","article-title":"Support vector method for novelty detection","volume":"12","author":"Sch\u00f6lkopf","year":"1999","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.108568_bib0051","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110725","article-title":"Robust anomaly detection for multivariate time series through temporal GCNs and attention-based VAE","volume":"275","author":"Shi","year":"2023","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.neunet.2026.108568_bib0052","series-title":"Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining","first-page":"1067","article-title":"Anomaly detection in streams with extreme value theory","author":"Siffer","year":"2017"},{"key":"10.1016\/j.neunet.2026.108568_bib0053","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2023.103094","article-title":"Gru-based interpretable multivariate time series anomaly detection in industrial control system","volume":"127","author":"Tang","year":"2023","journal-title":"Computers & Security"},{"key":"10.1016\/j.neunet.2026.108568_bib0054","series-title":"International conference on time series (ITISE)","first-page":"11","article-title":"Time series anomaly detection with discrete wavelet transforms and maximum likelihood estimation","volume":"vol. 2","author":"Thill","year":"2017"},{"key":"10.1016\/j.neunet.2026.108568_bib0055","series-title":"Proceedings of the 2023\u202fSIAM international conference on data mining (SDM)","first-page":"694","article-title":"Deep contrastive one-class time series anomaly detection","author":"Wang","year":"2023"},{"key":"10.1016\/j.neunet.2026.108568_bib0056","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122192","article-title":"Class-imbalanced time series anomaly detection method based on cost-sensitive hybrid network","volume":"238","author":"Wang","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.neunet.2026.108568_bib0057","first-page":"469","article-title":"Timexer: Empowering transformers for time series forecasting with exogenous variables","volume":"37","author":"Wang","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.108568_bib0058","series-title":"Proceedings of the ACM on web conference 2024","first-page":"3096","article-title":"Revisiting VAE for unsupervised time series anomaly detection: A frequency perspective","author":"Wang","year":"2024"},{"key":"10.1016\/j.neunet.2026.108568_bib0059","first-page":"69949","article-title":"Onenet: Enhancing time series forecasting models under concept drift by online ensembling","volume":"36","author":"Wen","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"10.1016\/j.neunet.2026.108568_bib0060","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1214\/23-EJS2208","article-title":"Stochastic online convex optimization. application to probabilistic time series forecasting","volume":"18","author":"Wintenberger","year":"2024","journal-title":"Electronic Journal of Statistics"},{"key":"10.1016\/j.neunet.2026.108568_bib0061","series-title":"International conference on learning representations","article-title":"Timesnet: Temporal 2d-variation modeling for general time series analysis","author":"Wu","year":"2023"},{"issue":"11","key":"10.1016\/j.neunet.2026.108568_bib0062","doi-asserted-by":"crossref","first-page":"5723","DOI":"10.1109\/TKDE.2024.3393996","article-title":"Calibrated one-class classification for unsupervised time series anomaly detection","volume":"36","author":"Xu","year":"2024","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.neunet.2026.108568_bib0063","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.neunet.2023.11.023","article-title":"Svd-ae: An asymmetric autoencoder with svd regularization for multivariate time series anomaly detection","volume":"170","author":"Yao","year":"2024","journal-title":"Neural Networks"},{"issue":"1","key":"10.1016\/j.neunet.2026.108568_bib0064","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3691338","article-title":"Deep learning for time series anomaly detection: A survey","volume":"57","author":"Zamanzadeh Darban","year":"2024","journal-title":"ACM Computing Surveys"},{"key":"10.1016\/j.neunet.2026.108568_bib0065","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.neunet.2023.12.023","article-title":"Acvae: A novel self-adversarial variational auto-encoder combined with contrast learning for time series anomaly detection","volume":"171","author":"Zhang","year":"2024","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.108568_bib0066","first-page":"463","article-title":"Suod: Accelerating large-scale unsupervised heterogeneous outlier detection","volume":"3","author":"Zhao","year":"2021","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"10.1016\/j.neunet.2026.108568_bib0067","series-title":"Proceedings of the 2019\u202fSIAM international conference on data mining","first-page":"585","article-title":"Lscp: Locally selective combination in parallel outlier ensembles","author":"Zhao","year":"2019"},{"key":"10.1016\/j.neunet.2026.108568_bib0068","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2022.113046","article-title":"A hybrid framework for forecasting power generation of multiple renewable energy sources","volume":"172","author":"Zheng","year":"2023","journal-title":"Renewable and Sustainable Energy Reviews"},{"key":"10.1016\/j.neunet.2026.108568_bib0069","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102255","article-title":"Graph spatiotemporal process for multivariate time series anomaly detection with missing values","volume":"106","author":"Zheng","year":"2024","journal-title":"Information Fusion"},{"key":"10.1016\/j.neunet.2026.108568_bib0070","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/j.neunet.2023.09.038","article-title":"Unsupervised anomaly detection by denselycontrastive learning for time series data","volume":"168","author":"Zhu","year":"2023","journal-title":"Neural Networks"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026000316?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026000316?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T18:37:05Z","timestamp":1775500625000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608026000316"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":70,"alternative-id":["S0893608026000316"],"URL":"https:\/\/doi.org\/10.1016\/j.neunet.2026.108568","relation":{},"ISSN":["0893-6080"],"issn-type":[{"value":"0893-6080","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"An online forecasting-based fine-tuning pipeline for time-series anomaly prediction","name":"articletitle","label":"Article Title"},{"value":"Neural Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neunet.2026.108568","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"108568"}}