{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T15:34:24Z","timestamp":1776440064192,"version":"3.51.2"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031735028","type":"print"},{"value":"9783031735035","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T00:00:00Z","timestamp":1731715200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T00:00:00Z","timestamp":1731715200000},"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\/978-3-031-73503-5_2","type":"book-chapter","created":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T03:59:18Z","timestamp":1731643158000},"page":"15-26","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Novel Integration of\u00a0Federated Learning and\u00a0LSTM for\u00a0Synthetic Time Series Generation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4759-1976","authenticated-orcid":false,"given":"Gurjot","family":"Singh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6928-0899","authenticated-orcid":false,"given":"Pritika","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8157-6274","authenticated-orcid":false,"given":"Jatin","family":"Bedi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,16]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Zhang, C., Kuppannagari, S.R., Kannan, R., Prasanna, V.K.: Generative adversarial network for synthetic time series data generation in smart grids. In: 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pp. 1\u20136. IEEE (2018)","DOI":"10.1109\/SmartGridComm.2018.8587464"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Yu, X., Karray, F.: Improving time series generation of GANs through soft dynamic time warping loss. In: 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3305\u20133310. IEEE (2022)","DOI":"10.1109\/SMC53654.2022.9945231"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Chowdhury, S.S., Boubrahimi, S.F., Hamdi, S.M.: Time series data augmentation using time-warped auto-encoders. In: 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 467\u2013470. IEEE (2021)","DOI":"10.1109\/ICMLA52953.2021.00111"},{"issue":"1","key":"2_CR4","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1109\/TNSM.2021.3098784","volume":"19","author":"C Wang","year":"2021","unstructured":"Wang, C., Wu, K., Zhou, T., Yu, G., Cai, Z.: Tsagen: synthetic time series generation for KPI anomaly detection. IEEE Trans. Netw. Serv. Manage. 19(1), 130\u2013145 (2021)","journal-title":"IEEE Trans. Netw. Serv. Manage."},{"key":"2_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109132","volume":"134","author":"A-A Semenoglou","year":"2023","unstructured":"Semenoglou, A.-A., Spiliotis, E., Assimakopoulos, V.: Data augmentation for univariate time series forecasting with neural networks. Pattern Recogn. 134, 109132 (2023)","journal-title":"Pattern Recogn."},{"issue":"2","key":"2_CR6","doi-asserted-by":"publisher","first-page":"1469","DOI":"10.1007\/s10489-022-03557-6","volume":"53","author":"J P\u00e9rez","year":"2023","unstructured":"P\u00e9rez, J., Arroba, P., Moya, J.M.: Data augmentation through multivariate scenario forecasting in data centers using generative adversarial networks. Appl. Intell. 53(2), 1469\u20131486 (2023)","journal-title":"Appl. Intell."},{"issue":"10","key":"2_CR7","doi-asserted-by":"publisher","first-page":"7834","DOI":"10.3390\/su15107834","volume":"15","author":"C-Y Tai","year":"2023","unstructured":"Tai, C.-Y., Wang, W.-J., Huang, Y.-M.: Using time-series generative adversarial networks to synthesize sensing data for pest incidence forecasting on sustainable agriculture. Sustainability 15(10), 7834 (2023)","journal-title":"Sustainability"},{"issue":"10","key":"2_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3559540","volume":"55","author":"E Brophy","year":"2023","unstructured":"Brophy, E., Wang, Z., She, Q., Ward, T.: Generative adversarial networks in time series: a systematic literature review. ACM Comput. Surv. 55(10), 1\u201331 (2023)","journal-title":"ACM Comput. Surv."},{"issue":"14","key":"2_CR9","doi-asserted-by":"publisher","first-page":"10123","DOI":"10.1007\/s00521-023-08459-3","volume":"35","author":"G Iglesias","year":"2023","unstructured":"Iglesias, G., Talavera, E., Gonz\u00e1lez-Prieto, \u00c1., Mozo, A., G\u00f3mez-Canaval, S.: Data augmentation techniques in time series domain: a survey and taxonomy. Neural Comput. Appl. 35(14), 10123\u201310145 (2023)","journal-title":"Neural Comput. Appl."},{"key":"2_CR10","unstructured":"Wen, Q., et al.: Time series data augmentation for deep learning: a survey. arXiv preprint arXiv:2002.12478 (2020)"},{"key":"2_CR11","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1016\/j.neucom.2020.12.132","volume":"456","author":"Q Wang","year":"2021","unstructured":"Wang, Q., Farahat, A., Gupta, C., Zheng, S.: Deep time series models for scarce data. Neurocomputing 456, 504\u2013518 (2021)","journal-title":"Neurocomputing"},{"issue":"10s","key":"2_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3502287","volume":"54","author":"MA Bansal","year":"2022","unstructured":"Bansal, M.A., Sharma, D.R., Kathuria, D.M.: A systematic review on data scarcity problem in deep learning: solution and applications. ACM Comput. Surv. 54(10s), 1\u201329 (2022)","journal-title":"ACM Comput. Surv."},{"key":"2_CR13","doi-asserted-by":"publisher","first-page":"120043","DOI":"10.1109\/ACCESS.2021.3107975","volume":"9","author":"K Choi","year":"2021","unstructured":"Choi, K., Yi, J., Park, C., Yoon, S.: Deep learning for anomaly detection in time-series data: review, analysis, and guidelines. IEEE Access 9, 120043\u2013120065 (2021)","journal-title":"IEEE Access"},{"issue":"6","key":"2_CR14","first-page":"90","volume":"1","author":"TM Kodinariya","year":"2013","unstructured":"Kodinariya, T.M., Makwana, P.R.: others: Review on determining number of cluster in K-means clustering. Int. J. 1(6), 90\u201395 (2013)","journal-title":"Int. J."},{"key":"2_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106854","volume":"149","author":"L Li","year":"2020","unstructured":"Li, L., Fan, Y., Tse, M., Lin, K.-Y.: A review of applications in federated learning. Comput. Indust. Eng. 149, 106854 (2020)","journal-title":"Comput. Indust. Eng."},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Syakur, M.A., Khotimah, B.K., Rochman, E.M.S., Satoto, B.D.: Integration k-means clustering method and elbow method for identification of the best customer profile cluster. In: IOP Conference Series: Materials Science and Engineering, vol. 336, p. 012017. IOP Publishing (2018)","DOI":"10.1088\/1757-899X\/336\/1\/012017"},{"issue":"7","key":"2_CR17","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Y Yu","year":"2019","unstructured":"Yu, Y., Si, X., Hu, C., Zhang, J.: A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 31(7), 1235\u20131270 (2019)","journal-title":"Neural Comput."}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73503-5_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T05:14:49Z","timestamp":1731647689000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73503-5_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,16]]},"ISBN":["9783031735028","9783031735035"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73503-5_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,16]]},"assertion":[{"value":"16 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Authors declare no Conflict of Interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Viana do Castelo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epia2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/epia2024.pt","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}