Self-adversarial variational autoencoder with spectral residual for time series anomaly detection
Detecting anomalies accurately in time series data has been receiving considerable attention due to its enormous potential for a wide array of applications. Numerous unsupervised anomaly detection methods for time series have been developed because of the difficulty of obtaining accurate labels. How...
Uloženo v:
| Vydáno v: | Neurocomputing (Amsterdam) Ročník 458; s. 349 - 363 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
11.10.2021
|
| Témata: | |
| ISSN: | 0925-2312, 1872-8286 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Detecting anomalies accurately in time series data has been receiving considerable attention due to its enormous potential for a wide array of applications. Numerous unsupervised anomaly detection methods for time series have been developed because of the difficulty of obtaining accurate labels. However, most existing unsupervised approaches suffer from the problem of anomaly contamination, which results in models that are unable to learn the normal pattern well and further deteriorate the performance of detection methods. To this end, a novel unsupervised method, called Self-adversarial Variational Autoencoder with Spectral Residual (SaVAE-SR), is introduced for time series anomaly detection in this paper. The SaVAE-SR first produces labels for unlabeled training data using the spectral residual technique to identify the most critical anomalies. A VAE model with a modified loss that can leverage label information to remove the influence of anomalous points is then trained in a self-adversarial manner, enabling the model to self-evaluate the learning of complex data distribution and improve itself accordingly. Specifically, the encoder acts as an encoder to approximate the posterior of latent variables and as a discriminator to evaluate the generative ability of the generator and improve itself accordingly. The generator is trained to capture the underlying data distribution and attempts to produce real samples to deceive the discriminator. The encoder and generator of the model compete with each other just like the behavior of GANs but work together under the theoretical framework of VAEs. As a result, the SaVAE-SR model combines the respective strengths of the VAE and adversarial training but does not require an additional discriminator, which makes the whole model very compact. Extensive experiments on five datasets demonstrate the superiority of the proposed method over the existing state-of-the-art methods. |
|---|---|
| AbstractList | Detecting anomalies accurately in time series data has been receiving considerable attention due to its enormous potential for a wide array of applications. Numerous unsupervised anomaly detection methods for time series have been developed because of the difficulty of obtaining accurate labels. However, most existing unsupervised approaches suffer from the problem of anomaly contamination, which results in models that are unable to learn the normal pattern well and further deteriorate the performance of detection methods. To this end, a novel unsupervised method, called Self-adversarial Variational Autoencoder with Spectral Residual (SaVAE-SR), is introduced for time series anomaly detection in this paper. The SaVAE-SR first produces labels for unlabeled training data using the spectral residual technique to identify the most critical anomalies. A VAE model with a modified loss that can leverage label information to remove the influence of anomalous points is then trained in a self-adversarial manner, enabling the model to self-evaluate the learning of complex data distribution and improve itself accordingly. Specifically, the encoder acts as an encoder to approximate the posterior of latent variables and as a discriminator to evaluate the generative ability of the generator and improve itself accordingly. The generator is trained to capture the underlying data distribution and attempts to produce real samples to deceive the discriminator. The encoder and generator of the model compete with each other just like the behavior of GANs but work together under the theoretical framework of VAEs. As a result, the SaVAE-SR model combines the respective strengths of the VAE and adversarial training but does not require an additional discriminator, which makes the whole model very compact. Extensive experiments on five datasets demonstrate the superiority of the proposed method over the existing state-of-the-art methods. |
| Author | Lin, Youfang Hu, Ganghui Wang, Jing Liu, Yunxiao Xiao, QinFeng |
| Author_xml | – sequence: 1 givenname: Yunxiao surname: Liu fullname: Liu, Yunxiao organization: School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China – sequence: 2 givenname: Youfang surname: Lin fullname: Lin, Youfang organization: School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China – sequence: 3 givenname: QinFeng surname: Xiao fullname: Xiao, QinFeng organization: School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China – sequence: 4 givenname: Ganghui surname: Hu fullname: Hu, Ganghui organization: School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China – sequence: 5 givenname: Jing surname: Wang fullname: Wang, Jing organization: School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China |
| BookMark | eNqFUE1LAzEUDFLBtvoPPOwf2DUv2WazHgQpfkHBg3oOMXmLKbubkqSV_ntT68mDnt7AmxlmZkYmox-RkEugFVAQV-tqxK3xQ8Uog4qKinJ6QqYgG1ZKJsWETGnLFiXjwM7ILMY1pdAAa6dEv2DfldruMEQdnO6L3eEk58eM9TZ5HI23GIpPlz6KuEGTQv4EjM5uM-h8KJIbsIgYHMZCj37Q_b6wmDI125yT0073ES9-7py83d-9Lh_L1fPD0_J2VRpORSoNdG0LuhYNWG5bLrXVYBm1rAbMWVEKCcI09h1kzTqrOW9M7rmoJbaZxufk-uhrgo8xYKeMS99FcmDXK6DqMJZaq-NY6jCWokJlkyyuf4k3wQ067P-T3RxlmIvtHAYVjcuDoXUht1fWu78NvgAmQYpN |
| CitedBy_id | crossref_primary_10_3390_s23021009 crossref_primary_10_1016_j_physa_2024_129602 crossref_primary_10_1016_j_asr_2024_09_050 crossref_primary_10_1109_TNSM_2023_3299846 crossref_primary_10_1177_09544070241227089 crossref_primary_10_1016_j_cosrev_2025_100787 crossref_primary_10_1016_j_ins_2023_119914 crossref_primary_10_1109_ACCESS_2024_3460971 crossref_primary_10_1109_ACCESS_2024_3516198 crossref_primary_10_1109_TNSM_2024_3353772 crossref_primary_10_1016_j_neucom_2023_126520 crossref_primary_10_1016_j_neucom_2022_06_042 crossref_primary_10_1109_ACCESS_2024_3361672 crossref_primary_10_1016_j_neucom_2024_127791 crossref_primary_10_1080_03772063_2024_2448588 crossref_primary_10_1016_j_neucom_2024_128441 crossref_primary_10_1089_big_2021_0471 |
| Cites_doi | 10.1007/978-3-030-30490-4_56 10.1145/3178876.3185996 10.1109/INFCOM.2012.6195498 10.1609/aaai.v32i1.11829 10.1109/IJCNN.2003.1223670 10.1145/2534169.2486035 10.1109/BigData50022.2020.9378139 10.1109/CVPR.2007.383267 10.1145/2815675.2815679 10.1109/INFCOM.2012.6195694 10.1109/TNNLS.2020.2980749 10.1002/stc.2136 10.1016/j.knosys.2019.105187 10.1561/2200000056 10.1023/A:1010933404324 10.1109/SSCI47803.2020.9308512 10.1111/j.2517-6161.1972.tb00912.x 10.1109/MNET.2009.4804320 10.1016/j.eswa.2018.01.037 10.1145/3292500.3330680 10.1109/PIC.2010.5687485 10.1016/j.eswa.2019.06.028 |
| ContentType | Journal Article |
| Copyright | 2021 Elsevier B.V. |
| Copyright_xml | – notice: 2021 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.neucom.2021.06.030 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-8286 |
| EndPage | 363 |
| ExternalDocumentID | 10_1016_j_neucom_2021_06_030 S0925231221009346 |
| GroupedDBID | --- --K --M .DC .~1 0R~ 123 1B1 1~. 1~5 4.4 457 4G. 53G 5VS 7-5 71M 8P~ 9JM 9JN AABNK AACTN AADPK AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXLA AAXUO AAYFN ABBOA ABCQJ ABFNM ABJNI ABMAC ABYKQ ACDAQ ACGFS ACRLP ACZNC ADBBV ADEZE AEBSH AEKER AENEX AFKWA AFTJW AFXIZ AGHFR AGUBO AGWIK AGYEJ AHHHB AHZHX AIALX AIEXJ AIKHN AITUG AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD AXJTR BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ IHE J1W KOM LG9 M41 MO0 MOBAO N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 ROL RPZ SDF SDG SDP SES SPC SPCBC SSN SSV SSZ T5K ZMT ~G- 29N 9DU AAQXK AATTM AAXKI AAYWO AAYXX ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EJD FEDTE FGOYB HLZ HVGLF HZ~ R2- SBC SEW WUQ XPP ~HD |
| ID | FETCH-LOGICAL-c306t-c1f991a4671d3d938ada1d20d241e712e86816c7db1842fda337c030548e920d3 |
| ISICitedReferencesCount | 19 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000691561400012&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0925-2312 |
| IngestDate | Sat Nov 29 07:13:46 EST 2025 Tue Nov 18 21:59:09 EST 2025 Fri Feb 23 02:43:48 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Variational autoencoder Adversarial training Spectral residual Anomaly detection Time series |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c306t-c1f991a4671d3d938ada1d20d241e712e86816c7db1842fda337c030548e920d3 |
| PageCount | 15 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_neucom_2021_06_030 crossref_primary_10_1016_j_neucom_2021_06_030 elsevier_sciencedirect_doi_10_1016_j_neucom_2021_06_030 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-10-11 |
| PublicationDateYYYYMMDD | 2021-10-11 |
| PublicationDate_xml | – month: 10 year: 2021 text: 2021-10-11 day: 11 |
| PublicationDecade | 2020 |
| PublicationTitle | Neurocomputing (Amsterdam) |
| PublicationYear | 2021 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Lin, Clark, Birke, Schönborn, Trigoni, Roberts (b0185) 2020 Alexander Geiger, Dongyu Liu, Sarah Alnegheimish, Alfredo Cuesta-Infante, Kalyan Veeramachaneni, Tadgan: time series anomaly detection using generative adversarial networks, in: 2020 IEEE International Conference on Big Data (IEEE BigData), IEEE, 2020. Sudipto Guha, Nina Mishra, Gourav Roy, Okke Schrijvers, Robust random cut forest based anomaly detection on streams, in: Proceedings of The 33rd International Conference on Machine Learning, vol. 48, 2016, pp. 2712–2721. Dan Li, Dacheng Chen, Jonathan Goh, See-kiong Ng, Anomaly detection with generative adversarial networks for multivariate time series. arXiv preprint arXiv:1809.04758, 2018. Kieu, Yang, Guo, Jensen (b0235) 2019 Suk-Bok Lee, Dan Pei, MohammadTaghi Hajiaghayi, Ioannis Pefkianakis, Songwu Lu, He Yan, Zihui Ge, Jennifer Yates, Mario Kosseifi, Threshold compression for 3g scalable monitoring, in: 2012 Proceedings IEEE INFOCOM, IEEE, 2012, pp. 1350–1358. Krishnamurthy, Sen, Zhang, Chen (b0105) 2003 Hundman, Constantinou, Laporte, Colwell, Soderstrom (b0180) 2018 Qi, Chu, He (b0120) 2018 Diederik P. Kingma, Max Welling, Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. Shanbhag, Wolf (b0145) 2009; 23 Lavin, Ahmad (b0295) 2015 Rui Zhang, Shaoyan Zhang, Sethuraman Muthuraman, Jianmin Jiang, One class support vector machine for anomaly detection in the communication network performance data, in: Proceedings of the 5th conference on Applied electromagnetics, wireless and optical communications, Citeseer, 2007, pp. 31–37. Pena, de Assis, Proença (b0115) 2013 Mihaela Rosca, Balaji Lakshminarayanan, Shakir Mohamed, Distribution matching in variational inference. arXiv preprint arXiv:1802.06847, 2018. Jordan Hochenbaum, Owen S. Vallis, Arun Kejariwal, Automatic anomaly detection in the cloud via statistical learning. arXiv preprint arXiv:1704.07706, 2017. Choffnes, Bustamante, Ge (b0100) 2010 Nguyen, Goulet (b0035) 2018; 25 Dan Li, Dacheng Chen, Baihong Jin, Lei Shi, Jonathan Goh, See-Kiong Ng, Mad-gan: multivariate anomaly detection for time series data with generative adversarial networks, in: International Conference on Artificial Neural Networks, Springer, 2019, pp. 703–716. Li, Yan, Wang, Jin (b0250) 2021; 32 Choudhary, Hiranandani, Saini (b0135) 2018 Haowen Xu, Wenxiao Chen, Nengwen Zhao, Zeyan Li, Jiahao Bu, Zhihan Li, Ying Liu, Youjian Zhao, Dan Pei, Yang Feng, et al., Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications, in: Proceedings of the 2018 World Wide Web Conference, pages 187–196. International World Wide Web Conferences Steering Committee, 2018. Bernhard Schölkopf, Robert C. Williamson, Alex J. Smola, John Shawe-Taylor, John C. Platt, Support vector method for novelty detection, in: Advances in Neural Information Processing Systems, 2000, pp. 582–588. Zhou, Liu, Hooi, Cheng, Ye (b0040) 2019 He Yan, Ashley Flavel, Zihui Ge, Alexandre Gerber, Dan Massey, Christos Papadopoulos, Hiren Shah, Jennifer Yates, Argus: end-to-end service anomaly detection and localization from an isp’s point of view, in: 2012 Proceedings IEEE INFOCOM, IEEE, 2012, pp. 2756–2760. Li, Chen, Pei (b0015) 2018 Md Abul Bashar, Richi Nayak, Tanogan: time series anomaly detection with generative adversarial networks. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2020, pp. 1778–1785. Chunyong Yin, Sun Zhang, Jin Wang, Neal N. Xiong, Anomaly detection based on convolutional recurrent autoencoder for iot time series, IEEE Trans. Syst. Man Cybern.: Syst. (2020) 1–11. Kingma, Welling (b0065) 2019; 12 Breunig, Kriegel, Ng, Sander (b0220) 2000 Nga Nguyen Thi, Van Loi Cao, Nhien-An Le-Khac, One-class collective anomaly detection based on long short-term memory recurrent neural networks. arXiv preprint arXiv:1802.00324, 2018. Guo, Liao, Wang, Lixing, Ji, Li (b0210) 2018; 95 Xiaodi Hou, Liqing Zhang, Saliency detection: a spectral residual approach, in: 2007 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2007, pp. 1–8. Huaibo Huang, Ran He, Zhenan Sun, Tieniu Tan, et al., Introvae: introspective variational autoencoders for photographic image synthesis, in: Advances in Neural Information Processing Systems, 2018, pp. 52–63. Tolga Ergen, Ali Hassan Mirza, and Suleyman Serdar Kozat. Unsupervised and semi-supervised anomaly detection with lstm neural networks. arXiv preprint arXiv:1710.09207, 2017. Ya, Zhao, Niu, Liu, Sun, Pei (b0205) 2019 Fontugne, Borgnat, Abry, Fukuda (b0140) 2010 Jurgovsky, Granitzer, Ziegler, Calabretto, Portier, He-Guelton, Caelen (b0045) 2018; 100 Audibert, Michiardi, Guyard, Marti, Zuluaga (b0245) 2020 Fox (b0080) 1972; 34 Ian Goodfellow, Nips 2016 tutorial: Generative adversarial networks. arXiv preprint arXiv:1701.00160, 2016. Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, Qi Zhang, Time-series anomaly detection service at microsoft, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, pp. 3009–3017. Workshop on Hot Topics in Cloud Computing (HotCloud 14), 2014. Dapeng Liu, Youjian Zhao, Haowen Xu, Yongqian Sun, Dan Pei, Jiao Luo, Xiaowei Jing, Mei Feng, Opprentice: Towards practical and automatic anomaly detection through machine learning, in: Proceedings of the 2015 Internet Measurement Conference, ACM, 2015, pp. 211–224. Breiman (b0165) 2001; 45 An, Cho (b0060) 2015; 2 Aditya Grover, Manik Dhar, Stefano Ermon, Flow-gan: Combining maximum likelihood and adversarial learning in generative models, in: Thirty-Second AAAI Conference on Artificial Intelligence, 2018. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, Soumith Chintala, Pytorch: an imperative style, high-performance deep learning library, in: H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, vol. 32. Curran Associates Inc, 2019. Junbo Zhao, Michael Mathieu, Yann LeCun, Energy-based generative adversarial networks, in: 5th International Conference on Learning Representations, ICLR 2017, 2017. Laptev, Amizadeh, Flint (b0050) 2015 Wang, Jing, Qi, Feng, Liao (b0155) 2019; 136 Leonid Portnoy, Intrusion detection with unlabeled data using clustering (Ph.D. thesis), Columbia University, 2000. Wang, Du, Lin, Cui, Shen, Yang (b0255) 2020; 190 Mingyan Teng, Anomaly detection on time series, in: 2010 IEEE International Conference on Progress in Informatics and Computing, vol. 1, IEEE, 2010, pp. 603–608. Owen Vallis, Jordan Hochenbaum, Arun Kejariwal, A novel technique for long-term anomaly detection in the cloud, in: 6th Siffer, Fouque, Termier, Largouet (b0125) 2017 Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, Generative adversarial nets, in: Advances in Neural Information Processing Systems, 2014, pp. 2672–2680. Junshui Ma, Simon Perkins, Time-series novelty detection using one-class support vector machines, in: Proceedings of the International Joint Conference on Neural Networks, 2003, vol. 3, IEEE, 2003, pp. 1741–1745. Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra, Stochastic backpropagation and approximate inference in deep generative models. arXiv preprint arXiv:1401.4082, 2014. Yingying Chen, Ratul Mahajan, Baskar Sridharan, Zhi-Li Zhang, A provider-side view of web search response time, in: ACM SIGCOMM Computer Communication Review, vol. 43, ACM, 2013, pp. 243–254. Kingma (10.1016/j.neucom.2021.06.030_b0065) 2019; 12 Qi (10.1016/j.neucom.2021.06.030_b0120) 2018 Choudhary (10.1016/j.neucom.2021.06.030_b0135) 2018 Kieu (10.1016/j.neucom.2021.06.030_b0235) 2019 Pena (10.1016/j.neucom.2021.06.030_b0115) 2013 10.1016/j.neucom.2021.06.030_b0190 10.1016/j.neucom.2021.06.030_b0070 10.1016/j.neucom.2021.06.030_b0030 Lavin (10.1016/j.neucom.2021.06.030_b0295) 2015 Breunig (10.1016/j.neucom.2021.06.030_b0220) 2000 10.1016/j.neucom.2021.06.030_b0230 Wang (10.1016/j.neucom.2021.06.030_b0255) 2020; 190 10.1016/j.neucom.2021.06.030_b0110 Guo (10.1016/j.neucom.2021.06.030_b0210) 2018; 95 10.1016/j.neucom.2021.06.030_b0275 Fox (10.1016/j.neucom.2021.06.030_b0080) 1972; 34 An (10.1016/j.neucom.2021.06.030_b0060) 2015; 2 10.1016/j.neucom.2021.06.030_b0270 10.1016/j.neucom.2021.06.030_b0150 10.1016/j.neucom.2021.06.030_b0075 10.1016/j.neucom.2021.06.030_b0195 10.1016/j.neucom.2021.06.030_b0005 Nguyen (10.1016/j.neucom.2021.06.030_b0035) 2018; 25 10.1016/j.neucom.2021.06.030_b0200 Hundman (10.1016/j.neucom.2021.06.030_b0180) 2018 Fontugne (10.1016/j.neucom.2021.06.030_b0140) 2010 Lin (10.1016/j.neucom.2021.06.030_b0185) 2020 Laptev (10.1016/j.neucom.2021.06.030_b0050) 2015 10.1016/j.neucom.2021.06.030_b0085 10.1016/j.neucom.2021.06.030_b0240 10.1016/j.neucom.2021.06.030_b0285 Wang (10.1016/j.neucom.2021.06.030_b0155) 2019; 136 10.1016/j.neucom.2021.06.030_b0280 Siffer (10.1016/j.neucom.2021.06.030_b0125) 2017 10.1016/j.neucom.2021.06.030_b0160 10.1016/j.neucom.2021.06.030_b0215 Jurgovsky (10.1016/j.neucom.2021.06.030_b0045) 2018; 100 Li (10.1016/j.neucom.2021.06.030_b0250) 2021; 32 Breiman (10.1016/j.neucom.2021.06.030_b0165) 2001; 45 Choffnes (10.1016/j.neucom.2021.06.030_b0100) 2010 Li (10.1016/j.neucom.2021.06.030_b0015) 2018 10.1016/j.neucom.2021.06.030_b0290 10.1016/j.neucom.2021.06.030_b0090 10.1016/j.neucom.2021.06.030_b0130 10.1016/j.neucom.2021.06.030_b0095 10.1016/j.neucom.2021.06.030_b0175 Zhou (10.1016/j.neucom.2021.06.030_b0040) 2019 10.1016/j.neucom.2021.06.030_b0170 Ya (10.1016/j.neucom.2021.06.030_b0205) 2019 10.1016/j.neucom.2021.06.030_b0055 10.1016/j.neucom.2021.06.030_b0010 Krishnamurthy (10.1016/j.neucom.2021.06.030_b0105) 2003 10.1016/j.neucom.2021.06.030_b0025 10.1016/j.neucom.2021.06.030_b0225 Audibert (10.1016/j.neucom.2021.06.030_b0245) 2020 10.1016/j.neucom.2021.06.030_b0265 Shanbhag (10.1016/j.neucom.2021.06.030_b0145) 2009; 23 10.1016/j.neucom.2021.06.030_b0260 10.1016/j.neucom.2021.06.030_b0020 |
| References_xml | – reference: Jordan Hochenbaum, Owen S. Vallis, Arun Kejariwal, Automatic anomaly detection in the cloud via statistical learning. arXiv preprint arXiv:1704.07706, 2017. – reference: Dan Li, Dacheng Chen, Baihong Jin, Lei Shi, Jonathan Goh, See-Kiong Ng, Mad-gan: multivariate anomaly detection for time series data with generative adversarial networks, in: International Conference on Artificial Neural Networks, Springer, 2019, pp. 703–716. – volume: 100 start-page: 234 year: 2018 end-page: 245 ident: b0045 article-title: Sequence classification for credit-card fraud detection publication-title: Expert Syst. Appl. – reference: Yingying Chen, Ratul Mahajan, Baskar Sridharan, Zhi-Li Zhang, A provider-side view of web search response time, in: ACM SIGCOMM Computer Communication Review, vol. 43, ACM, 2013, pp. 243–254. – reference: Huaibo Huang, Ran He, Zhenan Sun, Tieniu Tan, et al., Introvae: introspective variational autoencoders for photographic image synthesis, in: Advances in Neural Information Processing Systems, 2018, pp. 52–63. – reference: Aditya Grover, Manik Dhar, Stefano Ermon, Flow-gan: Combining maximum likelihood and adversarial learning in generative models, in: Thirty-Second AAAI Conference on Artificial Intelligence, 2018. – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: b0165 article-title: Random forests publication-title: Mach. Learn. – reference: Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra, Stochastic backpropagation and approximate inference in deep generative models. arXiv preprint arXiv:1401.4082, 2014. – reference: Leonid Portnoy, Intrusion detection with unlabeled data using clustering (Ph.D. thesis), Columbia University, 2000. – reference: Chunyong Yin, Sun Zhang, Jin Wang, Neal N. Xiong, Anomaly detection based on convolutional recurrent autoencoder for iot time series, IEEE Trans. Syst. Man Cybern.: Syst. (2020) 1–11. – start-page: 38 year: 2015 end-page: 44 ident: b0295 article-title: Evaluating real-time anomaly detection algorithms–the numenta anomaly benchmark publication-title: 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), IEEE – volume: 2 year: 2015 ident: b0060 article-title: Variational autoencoder based anomaly detection using reconstruction probability publication-title: Special Lecture on IE – start-page: 4322 year: 2020 end-page: 4326 ident: b0185 article-title: Anomaly detection for time series using vae-lstm hybrid model publication-title: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE – start-page: 234 year: 2003 end-page: 247 ident: b0105 article-title: Sketch-based change detection: methods, evaluation, and applications publication-title: Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement – volume: 190 year: 2020 ident: b0255 article-title: advae: a self-adversarial variational autoencoder with gaussian anomaly prior knowledge for anomaly detection publication-title: Knowl.-Based Syst. – reference: Haowen Xu, Wenxiao Chen, Nengwen Zhao, Zeyan Li, Jiahao Bu, Zhihan Li, Ying Liu, Youjian Zhao, Dan Pei, Yang Feng, et al., Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications, in: Proceedings of the 2018 World Wide Web Conference, pages 187–196. International World Wide Web Conferences Steering Committee, 2018. – reference: Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, Qi Zhang, Time-series anomaly detection service at microsoft, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, pp. 3009–3017. – reference: Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, Soumith Chintala, Pytorch: an imperative style, high-performance deep learning library, in: H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, vol. 32. Curran Associates Inc, 2019. – volume: 136 start-page: 94 year: 2019 end-page: 104 ident: b0155 article-title: Alsr: an adaptive label screening and relearning approach for interval-oriented anomaly detection publication-title: Expert Syst. Appl. – start-page: 1939 year: 2015 end-page: 1947 ident: b0050 article-title: Generic and scalable framework for automated time-series anomaly detection publication-title: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM – reference: Nga Nguyen Thi, Van Loi Cao, Nhien-An Le-Khac, One-class collective anomaly detection based on long short-term memory recurrent neural networks. arXiv preprint arXiv:1802.00324, 2018. – start-page: 548 year: 2018 end-page: 552 ident: b0120 article-title: Iterative anomaly detection algorithm based on time series analysis publication-title: 2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), IEEE – reference: Alexander Geiger, Dongyu Liu, Sarah Alnegheimish, Alfredo Cuesta-Infante, Kalyan Veeramachaneni, Tadgan: time series anomaly detection using generative adversarial networks, in: 2020 IEEE International Conference on Big Data (IEEE BigData), IEEE, 2020. – reference: Junbo Zhao, Michael Mathieu, Yann LeCun, Energy-based generative adversarial networks, in: 5th International Conference on Learning Representations, ICLR 2017, 2017. – start-page: 2828 year: 2019 end-page: 2837 ident: b0205 article-title: Robust anomaly detection for multivariate time series through stochastic recurrent neural network publication-title: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining – reference: Mingyan Teng, Anomaly detection on time series, in: 2010 IEEE International Conference on Progress in Informatics and Computing, vol. 1, IEEE, 2010, pp. 603–608. – reference: Dan Li, Dacheng Chen, Jonathan Goh, See-kiong Ng, Anomaly detection with generative adversarial networks for multivariate time series. arXiv preprint arXiv:1809.04758, 2018. – reference: Dapeng Liu, Youjian Zhao, Haowen Xu, Yongqian Sun, Dan Pei, Jiao Luo, Xiaowei Jing, Mei Feng, Opprentice: Towards practical and automatic anomaly detection through machine learning, in: Proceedings of the 2015 Internet Measurement Conference, ACM, 2015, pp. 211–224. – start-page: 522 year: 2018 end-page: 530 ident: b0135 article-title: Sparse decomposition for time series forecasting and anomaly detection publication-title: Proceedings of the 2018 SIAM International Conference on Data Mining, SIAM – reference: Workshop on Hot Topics in Cloud Computing (HotCloud 14), 2014. – reference: Ian Goodfellow, Nips 2016 tutorial: Generative adversarial networks. arXiv preprint arXiv:1701.00160, 2016. – start-page: 93 year: 2000 end-page: 104 ident: b0220 article-title: Lof: Identifying density-based local outliers publication-title: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data – reference: Diederik P. Kingma, Max Welling, Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. – reference: He Yan, Ashley Flavel, Zihui Ge, Alexandre Gerber, Dan Massey, Christos Papadopoulos, Hiren Shah, Jennifer Yates, Argus: end-to-end service anomaly detection and localization from an isp’s point of view, in: 2012 Proceedings IEEE INFOCOM, IEEE, 2012, pp. 2756–2760. – reference: Tolga Ergen, Ali Hassan Mirza, and Suleyman Serdar Kozat. Unsupervised and semi-supervised anomaly detection with lstm neural networks. arXiv preprint arXiv:1710.09207, 2017. – reference: Owen Vallis, Jordan Hochenbaum, Arun Kejariwal, A novel technique for long-term anomaly detection in the cloud, in: 6th – start-page: 1 year: 2010 end-page: 12 ident: b0140 article-title: Mawilab: combining diverse anomaly detectors for automated anomaly labeling and performance benchmarking publication-title: Proceedings of the 6th International COnference – reference: Xiaodi Hou, Liqing Zhang, Saliency detection: a spectral residual approach, in: 2007 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2007, pp. 1–8. – reference: Rui Zhang, Shaoyan Zhang, Sethuraman Muthuraman, Jianmin Jiang, One class support vector machine for anomaly detection in the communication network performance data, in: Proceedings of the 5th conference on Applied electromagnetics, wireless and optical communications, Citeseer, 2007, pp. 31–37. – reference: Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, Generative adversarial nets, in: Advances in Neural Information Processing Systems, 2014, pp. 2672–2680. – start-page: 3395 year: 2020 end-page: 3404 ident: b0245 article-title: Usad: unsupervised anomaly detection on multivariate time series publication-title: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining – reference: Mihaela Rosca, Balaji Lakshminarayanan, Shakir Mohamed, Distribution matching in variational inference. arXiv preprint arXiv:1802.06847, 2018. – start-page: 63 year: 2013 end-page: 66 ident: b0115 article-title: Anomaly detection using forecasting methods arima and hwds publication-title: 2013 32nd International Conference of the Chilean Computer Science Society (SCCC), IEEE – reference: Junshui Ma, Simon Perkins, Time-series novelty detection using one-class support vector machines, in: Proceedings of the International Joint Conference on Neural Networks, 2003, vol. 3, IEEE, 2003, pp. 1741–1745. – start-page: 4433 year: 2019 end-page: 4439 ident: b0040 article-title: Beatgan: anomalous rhythm detection using adversarially generated time series publication-title: Proceedings of the 28th International Joint Conference on Artificial Intelligence – reference: Suk-Bok Lee, Dan Pei, MohammadTaghi Hajiaghayi, Ioannis Pefkianakis, Songwu Lu, He Yan, Zihui Ge, Jennifer Yates, Mario Kosseifi, Threshold compression for 3g scalable monitoring, in: 2012 Proceedings IEEE INFOCOM, IEEE, 2012, pp. 1350–1358. – volume: 34 start-page: 350 year: 1972 end-page: 363 ident: b0080 article-title: Outliers in time series publication-title: J. Roy. Stat. Soc.: Ser. B (Methodol.) – reference: Sudipto Guha, Nina Mishra, Gourav Roy, Okke Schrijvers, Robust random cut forest based anomaly detection on streams, in: Proceedings of The 33rd International Conference on Machine Learning, vol. 48, 2016, pp. 2712–2721. – reference: Bernhard Schölkopf, Robert C. Williamson, Alex J. Smola, John Shawe-Taylor, John C. Platt, Support vector method for novelty detection, in: Advances in Neural Information Processing Systems, 2000, pp. 582–588. – volume: 95 start-page: 97 year: 2018 end-page: 112 ident: b0210 article-title: Multidimensional time series anomaly detection: a gru-based gaussian mixture variational autoencoder approach publication-title: Asian Conference on Machine Learning – reference: Md Abul Bashar, Richi Nayak, Tanogan: time series anomaly detection with generative adversarial networks. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2020, pp. 1778–1785. – volume: 23 start-page: 22 year: 2009 end-page: 28 ident: b0145 article-title: Accurate anomaly detection through parallelism publication-title: IEEE Network – start-page: 387 year: 2010 end-page: 398 ident: b0100 article-title: Crowdsourcing service-level network event monitoring publication-title: Proceedings of the ACM SIGCOMM 2010 Conference – volume: 25 year: 2018 ident: b0035 article-title: Anomaly detection with the switching kalman filter for structural health monitoring publication-title: Struct. Control Health Monit. – start-page: 2725 year: 2019 end-page: 2732 ident: b0235 article-title: Outlier detection for time series with recurrent autoencoder ensembles publication-title: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence – start-page: 387 year: 2018 end-page: 395 ident: b0180 article-title: Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding publication-title: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining – volume: 32 start-page: 1177 year: 2021 end-page: 1191 ident: b0250 article-title: Anomaly detection of time series with smoothness-inducing sequential variational auto-encoder publication-title: IEEE Trans. Neural Networks Learn. Syst. – volume: 12 start-page: 307 year: 2019 end-page: 392 ident: b0065 article-title: An introduction to variational autoencoders publication-title: Found. Trends Mach. Learn. – start-page: 1 year: 2018 end-page: 9 ident: b0015 article-title: Robust and unsupervised kpi anomaly detection based on conditional variational autoencoder publication-title: 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC), IEEE – start-page: 1067 year: 2017 end-page: 1075 ident: b0125 article-title: Anomaly detection in streams with extreme value theory publication-title: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – ident: 10.1016/j.neucom.2021.06.030_b0150 – ident: 10.1016/j.neucom.2021.06.030_b0195 doi: 10.1007/978-3-030-30490-4_56 – ident: 10.1016/j.neucom.2021.06.030_b0020 doi: 10.1145/3178876.3185996 – start-page: 234 year: 2003 ident: 10.1016/j.neucom.2021.06.030_b0105 article-title: Sketch-based change detection: methods, evaluation, and applications – ident: 10.1016/j.neucom.2021.06.030_b0240 – ident: 10.1016/j.neucom.2021.06.030_b0160 – ident: 10.1016/j.neucom.2021.06.030_b0070 – ident: 10.1016/j.neucom.2021.06.030_b0090 doi: 10.1109/INFCOM.2012.6195498 – ident: 10.1016/j.neucom.2021.06.030_b0075 doi: 10.1609/aaai.v32i1.11829 – start-page: 387 year: 2010 ident: 10.1016/j.neucom.2021.06.030_b0100 article-title: Crowdsourcing service-level network event monitoring – start-page: 93 year: 2000 ident: 10.1016/j.neucom.2021.06.030_b0220 article-title: Lof: Identifying density-based local outliers – ident: 10.1016/j.neucom.2021.06.030_b0005 doi: 10.1109/IJCNN.2003.1223670 – start-page: 548 year: 2018 ident: 10.1016/j.neucom.2021.06.030_b0120 article-title: Iterative anomaly detection algorithm based on time series analysis – ident: 10.1016/j.neucom.2021.06.030_b0095 doi: 10.1145/2534169.2486035 – volume: 95 start-page: 97 year: 2018 ident: 10.1016/j.neucom.2021.06.030_b0210 article-title: Multidimensional time series anomaly detection: a gru-based gaussian mixture variational autoencoder approach publication-title: Asian Conference on Machine Learning – ident: 10.1016/j.neucom.2021.06.030_b0230 doi: 10.1109/BigData50022.2020.9378139 – start-page: 1 year: 2018 ident: 10.1016/j.neucom.2021.06.030_b0015 article-title: Robust and unsupervised kpi anomaly detection based on conditional variational autoencoder – ident: 10.1016/j.neucom.2021.06.030_b0285 – start-page: 63 year: 2013 ident: 10.1016/j.neucom.2021.06.030_b0115 article-title: Anomaly detection using forecasting methods arima and hwds – ident: 10.1016/j.neucom.2021.06.030_b0130 – start-page: 387 year: 2018 ident: 10.1016/j.neucom.2021.06.030_b0180 article-title: Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding – volume: 2 issue: 1 year: 2015 ident: 10.1016/j.neucom.2021.06.030_b0060 article-title: Variational autoencoder based anomaly detection using reconstruction probability publication-title: Special Lecture on IE – ident: 10.1016/j.neucom.2021.06.030_b0200 – ident: 10.1016/j.neucom.2021.06.030_b0260 doi: 10.1109/CVPR.2007.383267 – ident: 10.1016/j.neucom.2021.06.030_b0010 – ident: 10.1016/j.neucom.2021.06.030_b0025 doi: 10.1145/2815675.2815679 – ident: 10.1016/j.neucom.2021.06.030_b0225 – ident: 10.1016/j.neucom.2021.06.030_b0270 – ident: 10.1016/j.neucom.2021.06.030_b0110 doi: 10.1109/INFCOM.2012.6195694 – ident: 10.1016/j.neucom.2021.06.030_b0215 – ident: 10.1016/j.neucom.2021.06.030_b0175 – volume: 32 start-page: 1177 issue: 3 year: 2021 ident: 10.1016/j.neucom.2021.06.030_b0250 article-title: Anomaly detection of time series with smoothness-inducing sequential variational auto-encoder publication-title: IEEE Trans. Neural Networks Learn. Syst. doi: 10.1109/TNNLS.2020.2980749 – start-page: 38 year: 2015 ident: 10.1016/j.neucom.2021.06.030_b0295 article-title: Evaluating real-time anomaly detection algorithms–the numenta anomaly benchmark – volume: 25 issue: 4 year: 2018 ident: 10.1016/j.neucom.2021.06.030_b0035 article-title: Anomaly detection with the switching kalman filter for structural health monitoring publication-title: Struct. Control Health Monit. doi: 10.1002/stc.2136 – start-page: 4322 year: 2020 ident: 10.1016/j.neucom.2021.06.030_b0185 article-title: Anomaly detection for time series using vae-lstm hybrid model – ident: 10.1016/j.neucom.2021.06.030_b0280 – ident: 10.1016/j.neucom.2021.06.030_b0290 – ident: 10.1016/j.neucom.2021.06.030_b0030 – start-page: 2828 year: 2019 ident: 10.1016/j.neucom.2021.06.030_b0205 article-title: Robust anomaly detection for multivariate time series through stochastic recurrent neural network – volume: 190 year: 2020 ident: 10.1016/j.neucom.2021.06.030_b0255 article-title: advae: a self-adversarial variational autoencoder with gaussian anomaly prior knowledge for anomaly detection publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2019.105187 – start-page: 4433 year: 2019 ident: 10.1016/j.neucom.2021.06.030_b0040 article-title: Beatgan: anomalous rhythm detection using adversarially generated time series – start-page: 522 year: 2018 ident: 10.1016/j.neucom.2021.06.030_b0135 article-title: Sparse decomposition for time series forecasting and anomaly detection – start-page: 3395 year: 2020 ident: 10.1016/j.neucom.2021.06.030_b0245 article-title: Usad: unsupervised anomaly detection on multivariate time series – start-page: 1 year: 2010 ident: 10.1016/j.neucom.2021.06.030_b0140 article-title: Mawilab: combining diverse anomaly detectors for automated anomaly labeling and performance benchmarking – start-page: 2725 year: 2019 ident: 10.1016/j.neucom.2021.06.030_b0235 article-title: Outlier detection for time series with recurrent autoencoder ensembles – volume: 12 start-page: 307 issue: 4 year: 2019 ident: 10.1016/j.neucom.2021.06.030_b0065 article-title: An introduction to variational autoencoders publication-title: Found. Trends Mach. Learn. doi: 10.1561/2200000056 – volume: 45 start-page: 5 issue: 1 year: 2001 ident: 10.1016/j.neucom.2021.06.030_b0165 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – ident: 10.1016/j.neucom.2021.06.030_b0190 doi: 10.1109/SSCI47803.2020.9308512 – ident: 10.1016/j.neucom.2021.06.030_b0275 – volume: 34 start-page: 350 issue: 3 year: 1972 ident: 10.1016/j.neucom.2021.06.030_b0080 article-title: Outliers in time series publication-title: J. Roy. Stat. Soc.: Ser. B (Methodol.) doi: 10.1111/j.2517-6161.1972.tb00912.x – volume: 23 start-page: 22 issue: 1 year: 2009 ident: 10.1016/j.neucom.2021.06.030_b0145 article-title: Accurate anomaly detection through parallelism publication-title: IEEE Network doi: 10.1109/MNET.2009.4804320 – start-page: 1067 year: 2017 ident: 10.1016/j.neucom.2021.06.030_b0125 article-title: Anomaly detection in streams with extreme value theory – volume: 100 start-page: 234 year: 2018 ident: 10.1016/j.neucom.2021.06.030_b0045 article-title: Sequence classification for credit-card fraud detection publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2018.01.037 – ident: 10.1016/j.neucom.2021.06.030_b0055 doi: 10.1145/3292500.3330680 – ident: 10.1016/j.neucom.2021.06.030_b0170 doi: 10.1109/PIC.2010.5687485 – ident: 10.1016/j.neucom.2021.06.030_b0265 – start-page: 1939 year: 2015 ident: 10.1016/j.neucom.2021.06.030_b0050 article-title: Generic and scalable framework for automated time-series anomaly detection – ident: 10.1016/j.neucom.2021.06.030_b0085 – volume: 136 start-page: 94 year: 2019 ident: 10.1016/j.neucom.2021.06.030_b0155 article-title: Alsr: an adaptive label screening and relearning approach for interval-oriented anomaly detection publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2019.06.028 |
| SSID | ssj0017129 |
| Score | 2.4542975 |
| Snippet | Detecting anomalies accurately in time series data has been receiving considerable attention due to its enormous potential for a wide array of applications.... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 349 |
| SubjectTerms | Adversarial training Anomaly detection Spectral residual Time series Variational autoencoder |
| Title | Self-adversarial variational autoencoder with spectral residual for time series anomaly detection |
| URI | https://dx.doi.org/10.1016/j.neucom.2021.06.030 |
| Volume | 458 |
| WOSCitedRecordID | wos000691561400012&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-8286 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017129 issn: 0925-2312 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9MwELdKxwMvbHyJ8SU_8DYZJXGTOI8V2gQTmkAMqW-RE9uQqU2nNqnKP8HfzJ3tpNmGBnvgJa0sx0l9v96dz-ffEfKWJ0kgeWiYFlIwsHgFk4UIWaFjDgZLloEytthEenYmZrPs82j0qzsLs5mndS222-zyv4oa2kDYeHT2DuLuB4UG-A5ChyuIHa7_JPivem6YxDrLa2lLcmzww4f8ZNsskboSGSRcdjqetFxZdv-1O5dl8w6rhT7Ct9VI4bxcyPnPI6Ubm7ZVD_1Zy-1R2soQPuYwXSD1gkKc9TGGT1VrVX1bbyu53LVajQf6xkhvP6F1Bj2w-UtVw7R-38HORvCh44-2GoYqIpsr51WpjzlGMQOH8or6nTjqdq9AuSMw9baYO-V3Q827iMPFu1q3mPODz7IsrH6L5wqr9jVr1-cgdultF7kbJcdRcszy48E9shelcSbGZG_68Xh22u9LpWHk2Bv9D-kOY9qMwZtv82dnZ-DAnB-Qh37lQacOMY_ISNePyX5X1YN6Jf-EyOsAogMA0QGAKAKIdgCiHYAoAIgigKgDEPUAoj2AnpJvJ8fn7z8wX4iDlbCibFgZGlhGSLCpoeIq40IqGaooUOD-aZgSLRIRJmWqilBMIqMk52mJlmQidAbd-DMyrpe1fk5oWATGRCaCgQvcopfgP4ukiINCGXBG40PCuxnLS89Sj8VS5vlt8jokrL_r0rG0_KV_2gkj956m8yBzQNitd76445Nekge7f8IrMm5WrX5N7pebplqv3nh4_QZIF6Uh |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Self-adversarial+variational+autoencoder+with+spectral+residual+for+time+series+anomaly+detection&rft.jtitle=Neurocomputing+%28Amsterdam%29&rft.au=Liu%2C+Yunxiao&rft.au=Lin%2C+Youfang&rft.au=Xiao%2C+QinFeng&rft.au=Hu%2C+Ganghui&rft.date=2021-10-11&rft.issn=0925-2312&rft.volume=458&rft.spage=349&rft.epage=363&rft_id=info:doi/10.1016%2Fj.neucom.2021.06.030&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_neucom_2021_06_030 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0925-2312&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0925-2312&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0925-2312&client=summon |