{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T10:13:54Z","timestamp":1783764834404,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819228584","type":"print"},{"value":"9789819228591","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T00:00:00Z","timestamp":1783814400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T00:00:00Z","timestamp":1783814400000},"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":[[2027]]},"DOI":"10.1007\/978-981-92-2859-1_19","type":"book-chapter","created":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T10:06:48Z","timestamp":1783764408000},"page":"255-267","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RA-TimesNet: A Residual\u2013Adaptive Framework for\u00a0Industrial Time-Series Anomaly Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3213-6083","authenticated-orcid":false,"given":"Xiaolong","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1448-8938","authenticated-orcid":false,"given":"Yong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,12]]},"reference":[{"key":"19_CR1","unstructured":"CISA: Compromise of U.S. Water Treatment Facility (Oldsmar). Cybersecurity Advisory AA21-042A (2021). https:\/\/www.cisa.gov\/news-events\/cybersecurity-advisories\/aa21-042a"},{"key":"19_CR2","doi-asserted-by":"publisher","unstructured":"Bl\u00e1zquez-Garc\u00eda, A., Conde, A., Mori, U., Lozano, J.A.: A review on outlier\/anomaly detection in time series data. ACM Comput. Surv. 54(1), Article 34 (2021). https:\/\/doi.org\/10.1145\/3444690","DOI":"10.1145\/3444690"},{"key":"19_CR3","unstructured":"An, J., Cho, S.: Variational autoencoder based anomaly detection using reconstruction probability. In: ICLR 2015 Workshop (2015)"},{"key":"19_CR4","unstructured":"Malhotra, P., Vig, L., Shroff, G., Agarwal, P.: Long short term memory networks for anomaly detection in time series. arXiv preprint arXiv:1607.00148 (2016)"},{"key":"19_CR5","unstructured":"Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018)"},{"key":"19_CR6","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zhang, S., et al.: Informer: beyond efficient Transformer for long sequence time-series forecasting. In: AAAI Conference on Artificial Intelligence (AAAI) (2021)","DOI":"10.1609\/aaai.v35i12.17325"},{"issue":"4","key":"19_CR7","doi-asserted-by":"publisher","first-page":"1748","DOI":"10.1016\/j.ijforecast.2021.03.012","volume":"37","author":"B Lim","year":"2021","unstructured":"Lim, B., Arik, S.O., Loeff, N., Pfister, T.: Temporal fusion transformers for interpretable multi-horizon time series forecasting. Int. J. Forecast. 37(4), 1748\u20131764 (2021)","journal-title":"Int. J. Forecast."},{"key":"19_CR8","volume-title":"Time Series Analysis: Forecasting and Control","author":"GEP Box","year":"2015","unstructured":"Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control, 5th edn. Wiley, Hoboken (2015)","edition":"5"},{"key":"19_CR9","volume-title":"Principal Component Analysis","author":"IT Jolliffe","year":"2002","unstructured":"Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer, New York (2002)","edition":"2"},{"key":"19_CR10","doi-asserted-by":"publisher","unstructured":"Campello, R.J.G.B., Moulavi, D., Zimek, A., Sander, J.: Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Trans. Knowl. Discov. Data 10(1), Article 5 (2015). https:\/\/doi.org\/10.1145\/2733381","DOI":"10.1145\/2733381"},{"issue":"7","key":"19_CR11","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1162\/089976601750264965","volume":"13","author":"B Sch\u00f6lkopf","year":"2001","unstructured":"Sch\u00f6lkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443\u20131471 (2001)","journal-title":"Neural Comput."},{"key":"19_CR12","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.-H.: Isolation forest. In: 2008 IEEE International Conference on Data Mining (ICDM), pp. 413\u2013422. IEEE (2008)","DOI":"10.1109\/ICDM.2008.17"},{"key":"19_CR13","unstructured":"Malhotra, P., Ramakrishnan, L., Anand, G., Vig, L., Agarwal, P., Shroff, G.: LSTM-based encoder\u2013decoder for multi-sensor anomaly detection. arXiv preprint arXiv:1607.00148 (2016)"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Kravchik, M., Shabtai, A.: Detecting cyberattacks in industrial control systems using convolutional neural networks. arXiv preprint arXiv:1806.08110 (2018)","DOI":"10.1145\/3264888.3264896"},{"key":"19_CR15","first-page":"102419","volume":"50","author":"MA Ferrag","year":"2020","unstructured":"Ferrag, M.A., Maglaras, L., Moschoyiannis, S., Janicke, H.: Deep learning for cyber security intrusion detection: approaches, datasets, and comparative study. J. Inf. Secur. Appl. 50, 102419 (2020)","journal-title":"J. Inf. Secur. Appl."},{"issue":"4","key":"19_CR16","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/MWC.2008.4599219","volume":"15","author":"S Rajasegarar","year":"2008","unstructured":"Rajasegarar, S., Leckie, C., Palaniswami, M.: Anomaly detection in wireless sensor networks. IEEE Wireless Commun. 15(4), 34\u201340 (2008)","journal-title":"IEEE Wireless Commun."},{"key":"19_CR17","unstructured":"Wu, H., Xu, J., Wang, J., Long, M.: AutoFormer: decomposition Transformers with auto-correlation for long-term series forecasting. arXiv preprint arXiv:2106.13008 (2021)"},{"key":"19_CR18","unstructured":"Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., Jin, R.: FEDformer: frequency enhanced decomposed Transformer for long-term series forecasting. arXiv preprint arXiv:2201.12740 (2022)"},{"key":"19_CR19","unstructured":"Xu, J., Wu, H., Wang, J., Long, M.: Anomaly transformer: time series anomaly detection with association discrepancy. arXiv preprint arXiv:2110.02642 (2021)"},{"key":"19_CR20","unstructured":"Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., Long, M.: TimesNet: temporal 2D-variation modeling for general time series analysis. arXiv preprint arXiv:2210.02186 (2022)"}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-2859-1_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T10:06:50Z","timestamp":1783764410000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-2859-1_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,12]]},"ISBN":["9789819228584","9789819228591"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-2859-1_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,12]]},"assertion":[{"value":"12 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","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":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2026","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":"ksem2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ksem2026.rosc.org.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}