{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T11:00:48Z","timestamp":1726225248929},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819947515"},{"type":"electronic","value":"9789819947522"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-4752-2_10","type":"book-chapter","created":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T16:02:10Z","timestamp":1690732930000},"page":"115-128","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Reinforced Active Learning for Time Series Anomaly Detection"],"prefix":"10.1007","author":[{"given":"Haojie","family":"Li","sequence":"first","affiliation":[]},{"given":"Hongzuo","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Peng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Yu, F., Xu, H., Jian, S., Huang, C., Wang, Y., Wu, Z.: Dram failure prediction inlarge-scale data centers. In: JCC, pp. 1\u20138 (2021)","DOI":"10.1109\/JCC53141.2021.00012"},{"key":"10_CR2","unstructured":"Kyle, H., Valentino, C., Christopher, L., Ian, C., Soderstrom, T.: Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding. In: KDD, pp. 387\u2013395 (2018)"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation-based anomaly detection. ACM Trans. Knowl. Discov. Data (TKDD) 6(1), 1\u201339 (2012)","DOI":"10.1145\/2133360.2133363"},{"issue":"6","key":"10_CR4","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.14778\/3514061.3514067","volume":"15","author":"S Tuli","year":"2022","unstructured":"Tuli, S., Casale, G., Jennings, N.R.: TranAD: deep transformer networks for anomaly detection in multivariate time series data. Proc. VLDB Endow. 15(6), 1201\u20131214 (2022)","journal-title":"Proc. VLDB Endow."},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Deng, A., Hooi, B.: Graph neural network-based anomaly detection in multivariate time series. In: AAAI, pp. 4027\u20134035 (2021)","DOI":"10.1609\/aaai.v35i5.16523"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Xu, H., Pang, G., Wang, Y., Wang, Y.: Deep isolation forest for anomaly detection In: IEEE Transactions on Knowledge and Data Engineering, pp. 1\u201314 (2023)","DOI":"10.1109\/TKDE.2023.3270293"},{"key":"10_CR7","unstructured":"Pang, G., Shen, C., Jin, H., van del Hengel, A.: Deep weakly-supervised anomaly detection. arXiv preprint arXiv:1910.13601 (2019)"},{"key":"10_CR8","unstructured":"Zhan, X., Wang, Q., Huang, K., Xiong, H., Dou, D., Chan, A.B.: A comparative survey of deep active learning. arXiv preprint arXiv:2203.13450 (2022)"},{"key":"10_CR9","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1613\/jair.3623","volume":"46","author":"N G\u00f6rnitz","year":"2013","unstructured":"G\u00f6rnitz, N., Kloft, M., Rieck, K., Brefeld, U.: Toward supervised anomaly detection. J. Artif. Intell. Res. 46, 235\u2013262 (2013)","journal-title":"J. Artif. Intell. Res."},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Huang, T., Chen, P., Li, R.: A semi-supervised VAE based active anomaly detection framework in multivariate time series for online systems. In: WWW, pp. 1797\u20131806 (2022)","DOI":"10.1145\/3485447.3511984"},{"key":"10_CR11","unstructured":"Wu, T., Ortiz, J.: RLAD: time series anomaly detection through reinforcement learning and active learning. arXiv preprint arXiv:2104.00543 (2021)"},{"issue":"7540","key":"10_CR12","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529\u2013533 (2015)","journal-title":"Nature"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21\u201327 (1967)","DOI":"10.1109\/TIT.1967.1053964"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Li, Z., Zhao, Y., Botta, N., Ionescu, C., Hu, X.: COPOD: copula-based outlier detection. In: ICDM, pp. 1118\u20131123 (2020)","DOI":"10.1109\/ICDM50108.2020.00135"},{"key":"10_CR15","unstructured":"Jiang, M., et al.: Weakly supervised anomaly detection: a survey. arXiv preprint arXiv:2302.04549 (2023)"},{"key":"10_CR16","unstructured":"Ruff, L., et al.: Deep semi-supervised anomaly detection. In: ICLR (2020)"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Pang, G., Shen, C., van den Hengel, A.: Deep anomaly detection with deviation networks. In: KDD, pp. 353\u2013362 (2019)","DOI":"10.1145\/3292500.3330871"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Laptev, N., Amizadeh, S., Flint, I.: Generic and scalable framework for automated time-series anomaly detection. In: KDD, pp. 1939\u20131947 (2015)","DOI":"10.1145\/2783258.2788611"},{"key":"10_CR19","unstructured":"AIOps competition (2018). https:\/\/github.com\/iopsai\/iops\/tree\/master\/phase2_env"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Pang, G., van den Hengel, A., Shen, C., Cao, L.: Toward deep supervised anomaly detection: reinforcement learning from partially labeled anomaly data. In: KDD, pp. 1298\u20131308 (2021)","DOI":"10.1145\/3447548.3467417"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Xu, H., et al.: Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications. In: WWW, pp. 187\u2013196 (2018)","DOI":"10.1145\/3178876.3185996"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Scheffer, T., Decomain, C., Wrobel, S.: Active hidden Markov models for information extraction. In: IDA, pp. 309\u2013318 (2001)","DOI":"10.1007\/3-540-44816-0_31"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, S., Qu, X., Shang, X.: Multi-instance discriminative contrastive learning for brain image representation. In: NCAA, pp. 1\u201314 (2022)","DOI":"10.1007\/s00521-022-07524-7"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, Y., An, R., Liu, S., Cui, J., Shang, X.: Predicting and understanding student learning performance using multi-source sparse attention convolutional neural networks. In: IEEE Transactions on Big Data, pp. 118\u2013132 (2023)","DOI":"10.1109\/TBDATA.2021.3125204"},{"key":"10_CR25","unstructured":"Xu, H., Wang, Y., Jian, S., Liao, Q., Wang, Y., Pang, G.: Calibrated one-class classification for unsupervised time series anomaly detection. arXiv preprint arXiv:2207.12201 (2022)"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-4752-2_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T23:08:05Z","timestamp":1690931285000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-4752-2_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819947515","9789819947522"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-4752-2_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhengzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2023a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2023\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}