{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T18:19:14Z","timestamp":1783102754088,"version":"3.54.6"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819699209","type":"print"},{"value":"9789819699216","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-9921-6_2","type":"book-chapter","created":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T06:15:16Z","timestamp":1753424116000},"page":"15-26","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["FreCT: Frequency-Augmented Convolutional Transformer for Robust Time Series Anomaly Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8916-6944","authenticated-orcid":false,"given":"Wenxin","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ding","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangzhen","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojian","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renxiang","family":"Guan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengze","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renda","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Xuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cuicui","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,26]]},"reference":[{"issue":"9","key":"2_CR1","doi-asserted-by":"publisher","first-page":"11802","DOI":"10.1109\/TNNLS.2023.3325667","volume":"35","author":"Y Zheng","year":"2023","unstructured":"Zheng, Y., et al.: Correlation-aware spatial-temporal graph learning for multivariate time-series anomaly detection. IEEE Transactions on Neural Networks and Learning Syst. 35(9), 11802\u201311816 (2023)","journal-title":"IEEE Transactions on Neural Networks and Learning Syst."},{"issue":"3","key":"2_CR2","doi-asserted-by":"publisher","first-page":"3891","DOI":"10.1109\/TII.2023.3314852","volume":"20","author":"B Yu","year":"2023","unstructured":"Yu, B., Yu, Y., Xu, J., Xiang, G., Yang, Z.: Mag: a novel approach for effective anomaly detection in spacecraft telemetry data. IEEE Trans. Industr. Inf. 20(3), 3891\u20133899 (2023)","journal-title":"IEEE Trans. Industr. Inf."},{"issue":"9","key":"2_CR3","doi-asserted-by":"publisher","first-page":"2895","DOI":"10.3390\/s24092895","volume":"24","author":"A Puder","year":"2024","unstructured":"Puder, A., Zink, M., Seidel, L., Sax, E.: Hybrid anomaly detection in time series by combining kalman filters and machine learning models. Sensors 24(9), 2895 (2024)","journal-title":"Sensors"},{"key":"2_CR4","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.inffus.2022.10.008","volume":"91","author":"G Li","year":"2023","unstructured":"Li, G., Jung, J.: Deep learning for anomaly detection in multivariate time series: approaches, applications, and challenges. Information Fusion 91, 93\u2013102 (2023)","journal-title":"Information Fusion"},{"key":"2_CR5","unstructured":"Xu, Z., Zeng, A., Xu, Q.: Fits: modeling time series with 10 k parameters. In 12th International Conference on Learning Representations (2023)"},{"key":"2_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2025.107399","volume":"187","author":"W Zhang","year":"2025","unstructured":"Zhang, W., Luo, C.: Decomposition-based multi-scale transformer framework for time series anomaly detection. Neural Netw. 187, 107399 (2025)","journal-title":"Neural Netw."},{"key":"2_CR7","first-page":"1","volume":"72","author":"W Shi","year":"2023","unstructured":"Shi, W., Karastoyanova, D., Ma, Y., Huang, Y., Zhang, G.: Clustering-based granular representation of time series with application to collective anomaly detection. IEEE Trans. Instrum. Meas. 72, 1\u201312 (2023)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"7","key":"2_CR8","doi-asserted-by":"publisher","first-page":"3365","DOI":"10.3390\/s23073365","volume":"23","author":"S Samudra","year":"2023","unstructured":"Samudra, S., Barbosh, M., Sadhu, A.: Machine learning-assisted improved anomaly detection for structural health monitoring. Sensors 23(7), 3365 (2023)","journal-title":"Sensors"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Wang, S., Wang, Y., Li, W.: Time series anomaly detection with a transformer residual autoencoder-decoder. In: International Conference on Neural Information Processing, pp. 512-524 (2023)","DOI":"10.1007\/978-981-99-8070-3_39"},{"key":"2_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.127791","volume":"592","author":"T Le","year":"2024","unstructured":"Le, T., Vu, H.C., Ponchet-Durupt, A., Boudaoud, N., Cherfi-Boulanger, Z., Nguyen-Trang, T.: Unsupervised detecting anomalies in multivariate time series by robust convolutional LSTM encoder-decoder. Neurocomputing 592, 127791 (2024)","journal-title":"Neurocomputing"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Lee, J., Park, B., Chae, D.K.: Duogat: dual time-oriented graph attention networks for accurate, efficient and explainable anomaly detection on time-series. In the 32nd ACM International Conference on Information and Knowledge Management, pp. 1188-1197 (2023)","DOI":"10.1145\/3583780.3614857"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Yang, Y., Zhang, C., Zhou, T., Wen, Q., Sun, L.: Dcdetector: dual attention contrastive representation learning for time series anomaly detection. In the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 3033-3045 (2023)","DOI":"10.1145\/3580305.3599295"},{"key":"2_CR13","doi-asserted-by":"publisher","first-page":"9881","DOI":"10.52202\/068431-0718","volume":"35","author":"Y Liu","year":"2022","unstructured":"Liu, Y., Wu, H., Wang, J., Long, M.: Non-stationary transformers: exploring the stationarity in time series forecasting. In the Advances in Neural Information Processing Systems 35, 9881\u20139893 (2022)","journal-title":"In the Advances in Neural Information Processing Systems"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Breunig, M. Kriegel, H., Ng, R., Sander, J.: LOF: identifying density-based local outliers. In: the ACM SIGMOD International Conference on Management of Data, pp. 93-104 (2000)","DOI":"10.1145\/342009.335388"},{"key":"2_CR15","unstructured":"Zong, B., Song, Q., Min, M., Cheng, W.: Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In the International Conference on Learning Representations (2018)"},{"key":"2_CR16","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In the International Conference on Learning Representations (2014)"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Su, Y., Zhao, Y., Niu, C., Liu, R., Sun, W., Pei, D.: Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2828-2837 (2019)","DOI":"10.1145\/3292500.3330672"},{"issue":"6","key":"2_CR18","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. Proceedings of the VLDB Endowment 15(6), 1201\u20131214 (2022)","journal-title":"Proceedings of the VLDB Endowment"},{"key":"2_CR19","unstructured":"Xu, J., Wu, H., Wang, J., Long, M.: Anomaly transformer: time series anomaly detection with association discrepancy. In the International Conference on Learning Representations (2022)"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Qin, S., Luo, Y., Tao, G.: Memory-augmented U-transformer for multivariate time series anomaly detection. In IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1-5 (2023)","DOI":"10.1109\/ICASSP49357.2023.10096179"},{"key":"2_CR21","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.neunet.2023.09.018","volume":"168","author":"J Fan","year":"2023","unstructured":"Fan, J., Wang, Z., Wu, H., Sun, D., Wu, F., Lu, X.: An adversarial time-frequency reconstruction network for unsupervised anomaly detection. Neural Netw. 168, 44\u201356 (2023)","journal-title":"Neural Netw."},{"key":"2_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2023.101949","volume":"56","author":"M Ma","year":"2023","unstructured":"Ma, M., Han, L., Zhou, C.: BTAD: a binary transformer deep neural network model for anomaly detection in multivariate time series data. Adv. Eng. Inform. 56, 101949 (2023)","journal-title":"Adv. Eng. Inform."},{"key":"2_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2024.102357","volume":"60","author":"C Wang","year":"2024","unstructured":"Wang, C., Liu, G.: From anomaly detection to classification with graph attention and transformer for multivariate time series. Adv. Eng. Inform. 60, 102357 (2024)","journal-title":"Adv. Eng. Inform."}],"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\/978-981-96-9921-6_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T17:44:35Z","timestamp":1783100675000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-9921-6_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819699209","9789819699216"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-9921-6_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"26 July 2025","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":"Ningbo","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}