{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T21:27:38Z","timestamp":1772141258147,"version":"3.50.1"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031466762","type":"print"},{"value":"9783031466779","type":"electronic"}],"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"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-46677-9_13","type":"book-chapter","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:02:29Z","timestamp":1699102949000},"page":"180-194","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["MTSTI: A Multi-task Learning Framework for\u00a0Spatiotemporal Imputation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8331-3410","authenticated-orcid":false,"given":"Yakun","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3561-3627","authenticated-orcid":false,"given":"Kaize","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9582-3445","authenticated-orcid":false,"given":"Xianzhi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4493-6663","authenticated-orcid":false,"given":"Guandong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","unstructured":"Acuna, E., Rodriguez, C.: The treatment of missing values and its effect on classifier accuracy. In: Banks, D., McMorris, F.R., Arabie, P., Gaul, W. (eds.) Classification, Clustering, and Data Mining Applications. Studies in Classification, Data Analysis, and Knowledge Organisation, pp. 639\u2013647. Springer, Berlin (2004). https:\/\/doi.org\/10.1007\/978-3-642-17103-1_60","DOI":"10.1007\/978-3-642-17103-1_60"},{"key":"13_CR2","series-title":"Lecture Notes in Statistics","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/978-1-4684-9403-7_2","volume-title":"Time Series Analysis of Irregularly Observed Data","author":"CF Ansley","year":"1984","unstructured":"Ansley, C.F., Kohn, R.: On the estimation of ARIMA models with missing values. In: Parzen, E. (ed.) Time Series Analysis of Irregularly Observed Data. Lecture Notes in Statistics, vol. 25, pp. 9\u201337. Springer, New York (1984). https:\/\/doi.org\/10.1007\/978-1-4684-9403-7_2"},{"issue":"1","key":"13_CR3","first-page":"1791","volume":"5","author":"P Arumugam","year":"2018","unstructured":"Arumugam, P., Saranya, R.: Outlier detection and missing value in seasonal ARIMA model using rainfall data. Mater. Today: Proc. 5(1), 1791\u20131799 (2018)","journal-title":"Mater. Today: Proc."},{"issue":"1","key":"13_CR4","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1002\/mpr.329","volume":"20","author":"MJ Azur","year":"2011","unstructured":"Azur, M.J., Stuart, E.A., Frangakis, C., Leaf, P.J.: Multiple imputation by chained equations: what is it and how does it work? Int. J. Methods Psychiatr. Res. 20(1), 40\u201349 (2011)","journal-title":"Int. J. Methods Psychiatr. Res."},{"issue":"7567","key":"13_CR5","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1038\/nature14956","volume":"525","author":"P Bauer","year":"2015","unstructured":"Bauer, P., Thorpe, A., Brunet, G.: The quiet revolution of numerical weather prediction. Nature 525(7567), 47\u201355 (2015)","journal-title":"Nature"},{"issue":"3","key":"13_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3444690","volume":"54","author":"A Bl\u00e1zquez-Garc\u00eda","year":"2021","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. (CSUR) 54(3), 1\u201333 (2021)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"13_CR7","unstructured":"Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: BRITS: bidirectional recurrent imputation for time series. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"issue":"1","key":"13_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-24271-9","volume":"8","author":"Z Che","year":"2018","unstructured":"Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Sci. Rep. 8(1), 1\u201312 (2018)","journal-title":"Sci. Rep."},{"issue":"9","key":"13_CR9","first-page":"4659","volume":"44","author":"X Chen","year":"2021","unstructured":"Chen, X., Sun, L.: Bayesian temporal factorization for multidimensional time series prediction. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 4659\u20134673 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"13_CR10","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1007\/978-981-99-1642-9_6","volume-title":"Neural Information Processing","author":"Y Chen","year":"2022","unstructured":"Chen, Y., Li, Z., Yang, C., Wang, X., Long, G., Xu, G.: Adaptive graph recurrent network for multivariate time series imputation. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds.) Neural Information Processing. Communications in Computer and Information Science, vol. 1792, pp. 64\u201373. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-99-1642-9_6"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Chen, Z., Jiaze, E., Zhang, X., Sheng, H., Cheng, X.: Multi-task time series forecasting with shared attention. In: 2020 International Conference on Data Mining Workshops (ICDMW), pp. 917\u2013925. IEEE (2020)","DOI":"10.1109\/ICDMW51313.2020.00132"},{"key":"13_CR12","unstructured":"Cini, A., Marisca, I., Alippi, C.: Filling the gaps: multivariate time series imputation by graph neural networks. arXiv preprint: arXiv:2108.00298 (2021)"},{"issue":"6","key":"13_CR13","doi-asserted-by":"publisher","first-page":"7833","DOI":"10.1109\/JSEN.2019.2923982","volume":"21","author":"Z Han","year":"2019","unstructured":"Han, Z., Zhao, J., Leung, H., Ma, K.F., Wang, W.: A review of deep learning models for time series prediction. IEEE Sens. J. 21(6), 7833\u20137848 (2019)","journal-title":"IEEE Sens. J."},{"key":"13_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"T Hastie","year":"2009","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.H., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, vol. 2. Springer, Cham (2009)"},{"issue":"4","key":"13_CR15","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","volume":"33","author":"H Ismail Fawaz","year":"2019","unstructured":"Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Deep learning for time series classification: a review. Data Min. Knowl. Disc. 33(4), 917\u2013963 (2019)","journal-title":"Data Min. Knowl. Disc."},{"key":"13_CR16","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1007\/978-3-030-47426-3_39","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"S Jawed","year":"2020","unstructured":"Jawed, S., Grabocka, J., Schmidt-Thieme, L.: Self-supervised learning for semi-supervised time series classification. In: Lauw, H.W., Wong, R.C.-W., Ntoulas, A., Lim, E.-P., Ng, S.-K., Pan, S.J. (eds.) PAKDD 2020. LNCS (LNAI), vol. 12084, pp. 499\u2013511. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-47426-3_39"},{"key":"13_CR17","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3389\/fdata.2020.00004","volume":"3","author":"S Kaushik","year":"2020","unstructured":"Kaushik, S., et al.: Ai in healthcare: time-series forecasting using statistical, neural, and ensemble architectures. Front. Big Data 3, 4 (2020)","journal-title":"Front. Big Data"},{"key":"13_CR18","unstructured":"Kreindler, D.M., Lumsden, C.J.: The effects of the irregular sample and missing data in time series analysis. In: Nonlinear Dynamics, Psychology, and Life Sciences (2006)"},{"key":"13_CR19","unstructured":"Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv preprint: arXiv:1707.01926 (2017)"},{"key":"13_CR20","unstructured":"Liu, Y., Yu, R., Zheng, S., Zhan, E., Yue, Y.: NAOMI: non-autoregressive multiresolution sequence imputation. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Ma, T., Tan, Y.: Multiple stock time series jointly forecasting with multi-task learning. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9207543"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Miao, X., Wu, Y., Wang, J., Gao, Y., Mao, X., Yin, J.: Generative semi-supervised learning for multivariate time series imputation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 8983\u20138991 (2021)","DOI":"10.1609\/aaai.v35i10.17086"},{"key":"13_CR23","doi-asserted-by":"crossref","unstructured":"Oehmcke, S., Zielinski, O., Kramer, O.: kNN ensembles with penalized DTW for multivariate time series imputation. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2774\u20132781. IEEE (2016)","DOI":"10.1109\/IJCNN.2016.7727549"},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using Markov chain monte Carlo. In: Proceedings of the 25th International Conference on Machine Learning, pp. 880\u2013887 (2008)","DOI":"10.1145\/1390156.1390267"},{"key":"13_CR25","unstructured":"Shang, C., Chen, J., Bi, J.: Discrete graph structure learning for forecasting multiple time series. arXiv preprint: arXiv:2101.06861 (2021)"},{"issue":"4","key":"13_CR26","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1111\/j.1467-9892.1982.tb00349.x","volume":"3","author":"RH Shumway","year":"1982","unstructured":"Shumway, R.H., Stoffer, D.S.: An approach to time series smoothing and forecasting using the EM algorithm. J. Time Ser. Anal. 3(4), 253\u2013264 (1982)","journal-title":"J. Time Ser. Anal."},{"key":"13_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-1494-6","volume-title":"Interpolation of Spatial Data: Some Theory for Kriging","author":"ML Stein","year":"1999","unstructured":"Stein, M.L.: Interpolation of Spatial Data: Some Theory for Kriging. Springer, Cham (1999)"},{"issue":"4","key":"13_CR28","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1002\/sim.4067","volume":"30","author":"IR White","year":"2011","unstructured":"White, I.R., Royston, P., Wood, A.M.: Multiple imputation using chained equations: issues and guidance for practice. Stat. Med. 30(4), 377\u2013399 (2011)","journal-title":"Stat. Med."},{"key":"13_CR29","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., Zhang, C.: Connecting the dots: Multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 753\u2013763 (2020)","DOI":"10.1145\/3394486.3403118"},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph WaveNet for deep spatial-temporal graph modeling. arXiv preprint: arXiv:1906.00121 (2019)","DOI":"10.24963\/ijcai.2019\/264"},{"key":"13_CR31","unstructured":"Yi, X., Zheng, Y., Zhang, J., Li, T.: ST-MVL: filling missing values in geo-sensory time series data. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence, IJCAI 2016, pp. 2704\u20132710 (2016)"},{"key":"13_CR32","unstructured":"Yoon, J., Jordon, J., Schaar, M.: Gain: missing data imputation using generative adversarial nets. In: International Conference on Machine Learning, pp. 5689\u20135698. PMLR (2018)"},{"key":"13_CR33","doi-asserted-by":"crossref","unstructured":"Yu, H., et al.: Regularized graph structure learning with semantic knowledge for multi-variates time-series forecasting. arXiv preprint: arXiv:2210.06126 (2022)","DOI":"10.24963\/ijcai.2022\/328"},{"key":"13_CR34","unstructured":"Yu, H.F., Rao, N., Dhillon, I.S.: Temporal regularized matrix factorization for high-dimensional time series prediction. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"13_CR35","doi-asserted-by":"publisher","first-page":"1609","DOI":"10.1007\/s00521-019-04212-x","volume":"32","author":"P Yu","year":"2020","unstructured":"Yu, P., Yan, X.: Stock price prediction based on deep neural networks. Neural Comput. Appl. 32, 1609\u20131628 (2020)","journal-title":"Neural Comput. Appl."},{"key":"13_CR36","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., Zhou, B., Cai, X., Guo, W., Ding, X., Yuan, X.: Missing value imputation in multivariate time series with end-to-end generative adversarial networks. Inf. Sci. 551, 67\u201382 (2021)","journal-title":"Inf. Sci."},{"issue":"3","key":"13_CR37","first-page":"1","volume":"5","author":"Y Zheng","year":"2014","unstructured":"Zheng, Y., Capra, L., Wolfson, O., Yang, H.: Urban computing: concepts, methodologies, and applications. ACM Transa. Intell. Syst. Technol. (TIST) 5(3), 1\u201355 (2014)","journal-title":"ACM Transa. Intell. Syst. Technol. (TIST)"},{"key":"13_CR38","doi-asserted-by":"publisher","unstructured":"Zivot, E., Wang, J.: Vector autoregressive models for multivariate time series. In: Zivot, E., Wang, J. (eds.) Modeling Financial Time Series with S-PLUS\u00ae, pp. 385\u2013429. Springer, New York (2006). https:\/\/doi.org\/10.1007\/978-0-387-32348-0_11","DOI":"10.1007\/978-0-387-32348-0_11"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46677-9_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:22:55Z","timestamp":1699104175000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46677-9_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031466762","9783031466779"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46677-9_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","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":"27 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2023.uqcloud.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes. Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"503","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"216","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"43% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.97","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.77","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}