{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:49:27Z","timestamp":1775666967250,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Missing values are ubiquitous in multivariate time series (MTS) data, posing significant challenges for accurate analysis and downstream applications. In recent years, deep learning-based methods have successfully handled missing data by leveraging complex temporal dependencies and learned data distributions. In this survey, we provide a comprehensive summary of deep learning approaches for multivariate time series imputation (MTSI) tasks. We propose a novel taxonomy that categorizes existing methods based on two key perspectives: imputation uncertainty and neural network architecture. Furthermore, we summarize existing MTSI toolkits with a particular emphasis on the PyPOTS Ecosystem, which provides an integrated and standardized foundation for MTSI research. Finally, we discuss key challenges and future research directions, which give insight for further MTSI research. This survey aims to serve as a valuable resource for researchers and practitioners in the field of time series analysis and missing data imputation tasks. A well-maintained MTSI paper and tool list is available at https:\/\/github.com\/WenjieDu\/Awesome_Imputation.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/1187","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"10696-10704","source":"Crossref","is-referenced-by-count":7,"title":["Deep Learning for Multivariate Time Series Imputation: A Survey"],"prefix":"10.24963","author":[{"given":"Jun","family":"Wang","sequence":"first","affiliation":[{"name":"PyPOTS Research"},{"name":"Hong Kong University of Science and Technology"},{"name":"Hong Kong University of Science and Technology (Guangzhou)"}]},{"given":"Wenjie","family":"Du","sequence":"additional","affiliation":[{"name":"PyPOTS Research"}]},{"given":"Yiyuan","family":"Yang","sequence":"additional","affiliation":[{"name":"PyPOTS Research"},{"name":"University of Oxford"}]},{"given":"Linglong","family":"Qian","sequence":"additional","affiliation":[{"name":"PyPOTS Research"},{"name":"King's College London"}]},{"given":"Wei","family":"Cao","sequence":"additional","affiliation":[{"name":"PyPOTS Research"}]},{"given":"Keli","family":"Zhang","sequence":"additional","affiliation":[{"name":"Huawei Noah\u2019s Ark Lab"}]},{"given":"Wenjia","family":"Wang","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology"},{"name":"Hong Kong University of Science and Technology (Guangzhou)"}]},{"given":"Yuxuan","family":"Liang","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology (Guangzhou)"}]},{"given":"Qingsong","family":"Wen","sequence":"additional","affiliation":[{"name":"Squirrel Ai Learning"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:36:23Z","timestamp":1758627383000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/1187"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/1187","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}