{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T15:07:20Z","timestamp":1781190440019,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819596935","type":"print"},{"value":"9789819596942","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-9694-2_16","type":"book-chapter","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T14:33:09Z","timestamp":1781188389000},"page":"221-237","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Phys-TSGAIN: A Physics-Informed Generative Imputation for\u00a0Data Completeness Governance of\u00a0Lithium-Ion Battery Time Series"],"prefix":"10.1007","author":[{"given":"Wei","family":"Zuo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyue","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siqi","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"issue":"2","key":"16_CR1","doi-asserted-by":"publisher","first-page":"75","DOI":"10.15415\/mjis.2013.12015","volume":"1","author":"S Singh","year":"2013","unstructured":"Singh, S.: Estimation of missing values in the data mining and comparison of imputation methods. Math. J. Interdiscip. Sci. 1(2), 75\u201390 (2013)","journal-title":"Math. J. Interdiscip. Sci."},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Prtama, I., et al.: A review of missing values handling methods on time-series data. In: Proceedings of the ICITSI, pp. 1\u20136 (2016)","DOI":"10.1109\/ICITSI.2016.7858189"},{"key":"16_CR3","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.neucom.2016.04.015","volume":"205","author":"M Amiri","year":"2016","unstructured":"Amiri, M., Jensen, R.: Missing data imputation using fuzzy-rough methods. Neurocomputing 205, 152\u2013164 (2016)","journal-title":"Neurocomputing"},{"key":"16_CR4","unstructured":"Gupta, S., Gupta, M.K.: A survey on different techniques for handling missing values in dataset. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. 4(1) (2018)"},{"issue":"1","key":"16_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/00401706.1974.10489142","volume":"16","author":"S Wold","year":"1974","unstructured":"Wold, S.: Spline functions in data analysis. Technometrics 16(1), 1\u201311 (1974)","journal-title":"Technometrics"},{"issue":"12","key":"16_CR6","first-page":"142","volume":"3","author":"WO Yodah","year":"2013","unstructured":"Yodah, W.O., et al.: Imputation of incomplete non-stationary seasonal time series data. Math. Theory Model. 3(12), 142\u2013154 (2013)","journal-title":"Math. Theory Model."},{"issue":"3","key":"16_CR7","first-page":"1","volume":"45","author":"S Van Buuren","year":"2010","unstructured":"Van Buuren, S., Groothuis-Oudshoorn, K.: MICE: multivariate imputation by chained equations in R. J. Stat. Softw. 45(3), 1\u201367 (2010)","journal-title":"J. Stat. Softw."},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"Zhang, X., et al.: Xgboost imputation for time series data. In: IEEE ICHI, pp. 1\u20133 (2019)","DOI":"10.1109\/ICHI.2019.8904666"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Pe\u00f1a, M., et al.: A novel imputation method for missing values in air pollutant time series data. In: IEEE LA-CCI, pp. 1\u20136 (2019)","DOI":"10.1109\/LA-CCI47412.2019.9037053"},{"key":"16_CR10","unstructured":"Vaishnav, R.L., Patel, K.M.: Analysis of various techniques to handling missing value in dataset. Int. J. Innov. Emerg. Res. Eng. 2(2) (2015)"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Jegadeeswari, K., et al.: Missing data imputation using ensemble learning technique: a review. Adv. Intell. Syst. Comput. 1428 (2023)","DOI":"10.1007\/978-981-19-3590-9_18"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Isil, B.E., et al.: MetaLIRS: meta-learning for imputation and regression selection. In: IDEAL 2024, Part I, pp. 155\u2013166 (2024)","DOI":"10.1007\/978-3-031-77731-8_15"},{"key":"16_CR13","first-page":"6775","volume":"2018","author":"W Cao","year":"2018","unstructured":"Cao, W., et al.: Brits: bidirectional recurrent imputation for time series. NeurIPS 2018, 6775\u20136785 (2018)","journal-title":"NeurIPS"},{"key":"16_CR14","unstructured":"Almeida, M.M., et al.: Univariate time series missing data imputation using Pix2Pix GAN. IEEE Lat. Am. Trans. 100 (2023)"},{"key":"16_CR15","doi-asserted-by":"publisher","first-page":"2517","DOI":"10.1109\/LSP.2022.3224880","volume":"29","author":"AY Yildiz","year":"2022","unstructured":"Yildiz, A.Y., et al.: Multivariate time series imputation with transformers. IEEE Signal Process. Lett. 29, 2517\u20132521 (2022)","journal-title":"IEEE Signal Process. Lett."},{"key":"16_CR16","unstructured":"Goswami, M., et al.: MOMENT: a family of open time-series foundation models. arXiv:2402.03885 (2024)"},{"key":"16_CR17","unstructured":"Yoon, J., et al.: GAIN: missing data imputation using generative adversarial nets. In: ICML 2018, PMLR vol. 80, pp. 5689\u20135698 (2018)"},{"key":"16_CR18","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.ins.2020.11.035","volume":"551","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., et al.: Missing value imputation in multivariate time series with end-to-end generative adversarial networks. Inf. Sci. 551, 67\u201382 (2021)","journal-title":"Inf. Sci."},{"key":"16_CR19","doi-asserted-by":"publisher","first-page":"112845","DOI":"10.1016\/j.asoc.2025.112845","volume":"172","author":"MA Mauricio","year":"2025","unstructured":"Mauricio, M.A., et al.: A meta-learning based neural network and LSTM for univariate time series missing data imputation. Appl. Soft Comput. 172, 112845 (2025)","journal-title":"Appl. Soft Comput."},{"issue":"1","key":"16_CR20","doi-asserted-by":"publisher","first-page":"137","DOI":"10.3390\/e25010137","volume":"25","author":"R Qin","year":"2023","unstructured":"Qin, R., Wang, Y.: ImputeGAN: generative adversarial network for multivariate time series imputation. Entropy 25(1), 137 (2023)","journal-title":"Entropy"}],"container-title":["Lecture Notes in Computer Science","Evaluation Science and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-9694-2_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T14:33:14Z","timestamp":1781188394000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-9694-2_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819596935","9789819596942"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-9694-2_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that the main data supporting the findings of this study are available from the corresponding author upon reasonable request.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Data and Code Availability"}},{"value":"Bench","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Benchmarking, Measuring and Optimization","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chengdu","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":"3 December 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bench2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.benchcouncil.org\/bench2025","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}