{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T01:40:35Z","timestamp":1763343635661,"version":"3.45.0"},"reference-count":33,"publisher":"Tech Science Press","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.063151","type":"journal-article","created":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T04:28:41Z","timestamp":1744345721000},"page":"843-860","source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised Anomaly Detection in Time Series Data via Enhanced VAE-Transformer Framework"],"prefix":"10.32604","volume":"84","author":[{"given":"Chunhao","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Bin","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Zhibin","family":"Huo","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"1808","DOI":"10.1109\/TNNLS.2020.3006738","article-title":"Entropic dynamic time warping kernels for co-evolving financial time series analysis","volume":"34","author":"Bai","year":"2023","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"108663","DOI":"10.1016\/j.engappai.2024.108663","article-title":"Automated financial time series anomaly detection via curiosity-guided exploration and self-imitation learning","volume":"135","author":"Cao","year":"2024","journal-title":"Eng Appl Artif Intell"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"116429","DOI":"10.1016\/j.eswa.2021.116429","article-title":"Financial fraud: a review of anomaly detection techniques and recent advances","volume":"193","author":"Hilal","year":"2022","journal-title":"Expert Syst Appl"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.ins.2022.10.060","article-title":"Anomaly detection in internet of medical things with blockchain from the perspective of deep neural network","volume":"617","author":"Wang","year":"2022","journal-title":"Inf Sci"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"102475","DOI":"10.1016\/j.media.2022.102475","article-title":"Unsupervised brain imaging 3D anomaly detection and segmentation with transformers","volume":"79","author":"Pinaya","year":"2022","journal-title":"Med Image Anal"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.jmsy.2023.08.013","article-title":"Multi-layer parallel transformer model for detecting product quality issues and locating anomalies based on multiple time-series process data in Industry 4.0","volume":"70","author":"Leng","year":"2023","journal-title":"J Manuf Syst"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"14718","DOI":"10.1038\/s41598-023-38447-5","article-title":"Machine learning-based anomaly detection of groundwater microdynamics: case study of Chengdu","volume":"13","author":"Shi","year":"2023","journal-title":"China Sci Rep"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"119479","DOI":"10.1016\/j.ins.2023.119479","article-title":"Varying-scale HCA-DBSCAN-based anomaly detection method for multi-dimensional energy data in steel industry","volume":"647","author":"Jin","year":"2023","journal-title":"Inf Sci"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"110826","DOI":"10.1016\/j.patcog.2024.110826","article-title":"Explainable time series anomaly detection using masked latent generative modeling","volume":"156","author":"Lee","year":"2024","journal-title":"Pattern Recognit"},{"key":"ref10","first-page":"517","article-title":"Unsupervised log anomaly detection method based on multi-feature","volume":"76","author":"He","year":"2023","journal-title":"Comput Mater Contin"},{"key":"ref11","first-page":"329","article-title":"Unsupervised anomaly detection approach based on adversarial memory autoencoders for multivariate time series","volume":"76","author":"Zhao","year":"2023","journal-title":"Comput Mater Contin"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"108945","DOI":"10.1016\/j.patcog.2022.108945","article-title":"Do deep neural networks contribute to multivariate time series anomaly detection?","volume":"132","author":"Audibert","year":"2022","journal-title":"Pattern Recognit"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.jhydrol.2017.02.058","article-title":"Identifying outliers of non-Gaussian groundwater state data based on ensemble estimation for long-term trends","volume":"548","author":"Jeong","year":"2017","journal-title":"J Hydrol"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"8590","DOI":"10.1109\/JIOT.2022.3214840","article-title":"Fast model update for iot traffic anomaly detection with machine unlearning","volume":"10","author":"Fan","year":"2023","journal-title":"IEEE Internet Things J"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"6481","DOI":"10.1109\/JIOT.2019.2958185","article-title":"Anomaly detection for iot time-series data: a survey","volume":"7","author":"Cook","year":"2020","journal-title":"IEEE Internet Things J"},{"key":"ref16","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":"Inf Fusion"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3460345","article-title":"A survey on automated log analysis for reliability engineering","volume":"54","author":"He","year":"2021","journal-title":"ACM Comput Surv"},{"key":"ref18","first-page":"1275","article-title":"Advancing autoencoder architectures for enhanced anomaly detection in multivariate industrial time series","volume":"81","author":"Lee","year":"2024","journal-title":"Comput Mater Contin"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput"},{"key":"ref20","series-title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","first-page":"1724","article-title":"Learning phrase representations using RNN encoder-decoder for statistical machine translation","author":"Cho","year":"2014 Oct 25\u201329"},{"key":"ref21","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","year":"2024","journal-title":"ACM Comput Surv"},{"key":"ref22","series-title":"2nd International Conference on Learning Representations (ICLR 2014)","article-title":"Auto-encoding variational bayes","author":"Kingma","year":"2014 Apr 14\u201316"},{"key":"ref23","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 Comput Surv"},{"key":"ref24","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"9593","article-title":"CausalVAE: disentangled representation learning via neural structural causal models","author":"Yang","year":"2021 Jun 20\u201325"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1109\/JIOT.2023.3289814","article-title":"Score-VAE: root cause analysis for federated-learning-based IoT anomaly detection","volume":"11","author":"Fan","year":"2024","journal-title":"IEEE Internet Things J"},{"key":"ref26","series-title":"ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"4322","article-title":"Anomaly detection for time series using VAE-LSTM hybrid model","author":"Lin","year":"2020 May 4\u20138"},{"key":"ref27","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":"Appl Intell"},{"key":"ref28","first-page":"123","article-title":"LSTM-GAN: unsupervised anomaly detection for time series fusion of GAN and Bi-LSTM","volume":"45","author":"Chen","year":"2024","journal-title":"J Chin Comput Syst"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"98115","DOI":"10.1109\/ACCESS.2023.3313448","article-title":"Anomaly VAE-transformer: a deep learning approach for anomaly detection in decentralized finance","volume":"11","author":"Song","year":"2023","journal-title":"IEEE Access"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"120852","DOI":"10.1016\/j.ins.2024.120852","article-title":"VAEAT: variational AutoeEncoder with adversarial training for multivariate time series anomaly detection","volume":"676","author":"He","year":"2024","journal-title":"Inf Sci"},{"key":"ref31","series-title":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","first-page":"38","article-title":"Evaluating real-time anomaly detection algorithms\u2014the Numenta anomaly benchmark","author":"Lavin","year":"2015 Dec 9\u201311"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.neucom.2017.04.070","article-title":"Unsupervised real-time anomaly detection for streaming data","volume":"262","author":"Ahmad","year":"2017","journal-title":"Neurocomputing"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"2202","DOI":"10.3390\/app14052202","article-title":"Anomaly detection and identification method for shield tunneling based on energy consumption perspective","volume":"14","author":"Hu","year":"2024","journal-title":"Appl Sci"}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-84-1\/TSP_CMC_63151\/TSP_CMC_63151.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T01:36:28Z","timestamp":1763343388000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v84n1\/61729"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.063151","relation":{},"ISSN":["1546-2226"],"issn-type":[{"type":"electronic","value":"1546-2226"}],"subject":[],"published":{"date-parts":[[2025]]}}}