{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T21:08:37Z","timestamp":1774991317806,"version":"3.50.1"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030893620","type":"print"},{"value":"9783030893637","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-89363-7_39","type":"book-chapter","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T01:02:59Z","timestamp":1635728579000},"page":"515-529","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Reconstruct Anomaly to Normal: Adversarially Learned and Latent Vector-Constrained Autoencoder for Time-Series Anomaly Detection"],"prefix":"10.1007","author":[{"given":"Chunkai","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaocong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peiyi","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanyi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,11,1]]},"reference":[{"key":"39_CR1","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/j.future.2015.01.001","volume":"55","author":"M Ahmed","year":"2016","unstructured":"Ahmed, M., Mahmood, A.N., Islam, M.R.: A survey of anomaly detection techniques in financial domain. Future Gener. Comput. Syst. 55, 278\u2013288 (2016)","journal-title":"Future Gener. Comput. Syst."},{"issue":"3","key":"39_CR2","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1016\/S0735-1097(86)80478-8","volume":"7","author":"DS Baim","year":"1986","unstructured":"Baim, D.S., et al.: Survival of patients with severe congestive heart failure treated with oral milrinone. J. Am. Coll. Cardiol. 7(3), 661\u2013670 (1986)","journal-title":"J. Am. Coll. Cardiol."},{"key":"39_CR3","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93\u2013104 (2000)","DOI":"10.1145\/335191.335388"},{"key":"39_CR4","doi-asserted-by":"crossref","unstructured":"Chalapathy, R., Chawla, S.: Deep learning for anomaly detection: a survey. arXiv preprint arXiv:1901.03407 (2019)","DOI":"10.1145\/3394486.3406704"},{"issue":"3","key":"39_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 1\u201358 (2009)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"39_CR6","doi-asserted-by":"crossref","unstructured":"Chauhan, S., Vig, L.: Anomaly detection in ECG time signals via deep long short-term memory networks. In: 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1\u20137. IEEE (2015)","DOI":"10.1109\/DSAA.2015.7344872"},{"key":"39_CR7","unstructured":"Chen, Y., et al.: The UCR time series classification archive (2015)"},{"issue":"23","key":"39_CR8","doi-asserted-by":"publisher","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","volume":"101","author":"AL Goldberger","year":"2000","unstructured":"Goldberger, A.L., et al.: Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215\u2013e220 (2000)","journal-title":"Circulation"},{"key":"39_CR9","doi-asserted-by":"crossref","unstructured":"Gong, D., et al.: Memorizing normality to detect anomaly: memory-augmented deep autoencoder for unsupervised anomaly detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1705\u20131714 (2019)","DOI":"10.1109\/ICCV.2019.00179"},{"key":"39_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1007\/3-540-46145-0_17","volume-title":"Data Warehousing and Knowledge Discovery","author":"S Hawkins","year":"2002","unstructured":"Hawkins, S., He, H., Williams, G., Baxter, R.: Outlier detection using replicator neural networks. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2002. LNCS, vol. 2454, pp. 170\u2013180. Springer, Heidelberg (2002). https:\/\/doi.org\/10.1007\/3-540-46145-0_17"},{"issue":"3","key":"39_CR11","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/PL00011669","volume":"3","author":"E Keogh","year":"2001","unstructured":"Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. Knowl. Inf. Syst. 3(3), 263\u2013286 (2001)","journal-title":"Knowl. Inf. Syst."},{"key":"39_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1007\/978-3-030-30490-4_56","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Text and Time Series","author":"D Li","year":"2019","unstructured":"Li, D., Chen, D., Jin, B., Shi, L., Goh, J., Ng, S.K.: MAD-GAN: multivariate anomaly detection for time series data with generative adversarial networks. In: Tetko, I.V., K\u016frkov\u00e1, V., Karpov, P., Theis, F. (eds.) ICANN 2019. LNCS, vol. 11730, pp. 703\u2013716. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30490-4_56"},{"key":"39_CR13","doi-asserted-by":"crossref","unstructured":"Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 2\u201311 (2003)","DOI":"10.1145\/882082.882086"},{"issue":"2","key":"39_CR14","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s10618-007-0064-z","volume":"15","author":"J Lin","year":"2007","unstructured":"Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing sax: a novel symbolic representation of time series. Data Min. Knowl. Discovery 15(2), 107\u2013144 (2007)","journal-title":"Data Min. Knowl. Discovery"},{"key":"39_CR15","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413\u2013422. IEEE (2008)","DOI":"10.1109\/ICDM.2008.17"},{"key":"39_CR16","doi-asserted-by":"crossref","unstructured":"Lkhagva, B., Yu, S., Kawagoe, K.: New time series data representation esax for financial applications. In: International Conference on Data Engineering Workshops, pp. 115\u2013126 (2006)","DOI":"10.1109\/ICDEW.2006.99"},{"key":"39_CR17","unstructured":"Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., Shroff, G.: LSTM-based encoder-decoder for multi-sensor anomaly detection. arXiv preprint arXiv:1607.00148 (2016)"},{"key":"39_CR18","unstructured":"Malhotra, P., Vig, L., Shroff, G., Agarwal, P.: Long short term memory networks for anomaly detection in time series. In: Proceedings, vol. 89, pp. 89\u201394. Presses universitaires de Louvain (2015)"},{"issue":"3","key":"39_CR19","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/51.932724","volume":"20","author":"GB Moody","year":"2001","unstructured":"Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45\u201350 (2001)","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"39_CR20","doi-asserted-by":"crossref","unstructured":"Moonesignhe, H., Tan, P.N.: Outlier detection using random walks. In: 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2006), pp. 532\u2013539. IEEE (2006)","DOI":"10.1109\/ICTAI.2006.94"},{"key":"39_CR21","doi-asserted-by":"crossref","unstructured":"Nicolau, M., McDermott, J., et al.: Learning neural representations for network anomaly detection. IEEE Trans. Ccybernet. 49(8), 3074\u20133087 (2018)","DOI":"10.1109\/TCYB.2018.2838668"},{"key":"39_CR22","doi-asserted-by":"crossref","unstructured":"Pang, G., Cao, L., Chen, L., Liu, H.: Learning representations of ultrahigh-dimensional data for random distance-based outlier detection. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2041\u20132050 (2018)","DOI":"10.1145\/3219819.3220042"},{"key":"39_CR23","doi-asserted-by":"crossref","unstructured":"Perera, P., Nallapati, R., Xiang, B.: Ocgan: One-class novelty detection using gans with constrained latent representations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2898\u20132906 (2019)","DOI":"10.1109\/CVPR.2019.00301"},{"key":"39_CR24","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.knosys.2017.07.021","volume":"135","author":"H Ren","year":"2017","unstructured":"Ren, H., Liu, M., Li, Z., Pedrycz, W.: A piecewise aggregate pattern representation approach for anomaly detection in time series. Knowl. Based Syst. 135, 29\u201339 (2017)","journal-title":"Knowl. Based Syst."},{"issue":"5","key":"39_CR25","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1002\/tee.22626","volume":"13","author":"H Ren","year":"2018","unstructured":"Ren, H., Liu, M., Liao, X., Liang, L., Ye, Z., Li, Z.: Anomaly detection in time series based on interval sets. IEEJ Trans. Electr. Electron. Eng. 13(5), 757\u2013762 (2018)","journal-title":"IEEJ Trans. Electr. Electron. Eng."},{"key":"39_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1007\/978-3-319-59050-9_12","volume-title":"Information Processing in Medical Imaging","author":"T Schlegl","year":"2017","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 146\u2013157. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_12"},{"key":"39_CR27","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.neucom.2014.01.045","volume":"138","author":"Y Sun","year":"2014","unstructured":"Sun, Y., Li, J., Liu, J., Sun, B., Chow, C.: An improvement of symbolic aggregate approximation distance measure for time series. Neurocomputing 138, 189\u2013198 (2014)","journal-title":"Neurocomputing"},{"key":"39_CR28","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.neucom.2017.02.039","volume":"241","author":"B Tang","year":"2017","unstructured":"Tang, B., He, H.: A local density-based approach for outlier detection. Neurocomputing 241, 171\u2013180 (2017)","journal-title":"Neurocomputing"},{"key":"39_CR29","doi-asserted-by":"crossref","unstructured":"Zenati, H., Romain, M., Foo, C.S., Lecouat, B., Chandrasekhar, V.: Adversarially learned anomaly detection. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 727\u2013736. IEEE (2018)","DOI":"10.1109\/ICDM.2018.00088"},{"issue":"4","key":"39_CR30","doi-asserted-by":"publisher","first-page":"2154","DOI":"10.3934\/mbe.2019105","volume":"16","author":"C Zhang","year":"2019","unstructured":"Zhang, C., Chen, Y., Yin, A., Wang, X.: Anomaly detection in ECG based on trend symbolic aggregate approximation. Math. Biosci. Eng. 16(4), 2154\u20132167 (2019)","journal-title":"Math. Biosci. Eng."},{"key":"39_CR31","doi-asserted-by":"crossref","unstructured":"Zimek, A., Schubert, E., Kriegel, H.P.: A survey on unsupervised outlier detection in high-dimensional numerical data. Stat. Anal. Data Min. ASA Data Sci. J. 5(5), 363\u2013387 (2012)","DOI":"10.1002\/sam.11161"},{"key":"39_CR32","unstructured":"Zong, B., et al.: Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: International Conference on Learning Representations (2018)"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2021: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-89363-7_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T01:22:59Z","timestamp":1635729779000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-89363-7_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030893620","9783030893637"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-89363-7_39","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanoi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2021","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"382","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"93","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}