{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:15:00Z","timestamp":1758672900216,"version":"3.44.0"},"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>Multivariate time series (MTS) data in real-world scenarios are often incomplete, which hinders effective data analysis. Therefore, MTS imputation has been widely studied to facilitate various MTS tasks. Existing imputation methods primarily initialize missing values with zeros in order to perform effective incomplete MTS encoding, which impede the model's capacity to precisely discern the missing distribution. Moreover, these methods often overlook the global similarity in time series but are limited in the use of local information within the sample. To this end, we propose a novel multivariate time series imputation network model, named MMNet. MMNet introduces a Missing-Aware Embedding (MAE) approach to adaptively represent incomplete MTS, allowing the model to better distinguish between missing and observed data. Furthermore, we design a Memory-Enhanced Encoder (MEE) aimed at modeling prior knowledge through memory mechanism, enabling better utilization of the global similarity within the time series. Building upon this, MMNet incorporates a Multi-scale Mixing architecture (MSM) that leverages information from multiple scales to enhance the final imputation. Extensive experiments on four public real-world datasets demonstrate that, MMNet yields a more than 25% gain in performance, compared with the state-of-the-art methods.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/357","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"3208-3216","source":"Crossref","is-referenced-by-count":0,"title":["MMNet: Missing-Aware and Memory-Enhanced Network for Multivariate Time Series Imputation"],"prefix":"10.24963","author":[{"given":"Xiaoye","family":"Miao","sequence":"first","affiliation":[{"name":"Center for Data Science, Zhejiang University"}]},{"given":"Han","family":"Shi","sequence":"additional","affiliation":[{"name":"Center for Data Science, Zhejiang University"}]},{"given":"Yi","family":"Yuan","sequence":"additional","affiliation":[{"name":"Software College, Zhejiang University"}]},{"given":"Daozhan","family":"Pan","sequence":"additional","affiliation":[{"name":"Software College, Zhejiang University"}]},{"given":"Yangyang","family":"Wu","sequence":"additional","affiliation":[{"name":"Software College, Zhejiang University"},{"name":"Binjiang Institute of Zhejiang University"}]},{"given":"Xiaohua","family":"Pan","sequence":"additional","affiliation":[{"name":"Software College, Zhejiang University"},{"name":"Binjiang Institute of Zhejiang University"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","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":"2025-09-23T11:33:48Z","timestamp":1758627228000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/357"}},"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\/357","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}