{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T10:21:00Z","timestamp":1778149260820,"version":"3.51.4"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI-SS"],"abstract":"<jats:p>Time-series modeling is essential for applications in agriculture, weather forecasting, food production, and more. However, missing data due to sensor malfunctions, power outages, and human errors is a common issue, complicating the training of machine learning models. We propose a diffusion-based generative model to\naddress this problem and fill the gaps in the data. Our approach captures feature and time correlations through a two-stage imputation process. Our model outperforms state-of-the-art imputation methods and is more scalable in GPU resources.<\/jats:p>","DOI":"10.1609\/aaaiss.v4i1.31827","type":"journal-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T11:45:28Z","timestamp":1731066328000},"page":"424-431","source":"Crossref","is-referenced-by-count":1,"title":["Self-attention-based Diffusion Model for Time-series Imputation"],"prefix":"10.1609","volume":"4","author":[{"given":"Mohammad Rafid Ul","family":"Islam","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prasad","family":"Tadepalli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alan","family":"Fern","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2024,11,8]]},"container-title":["Proceedings of the AAAI Symposium Series"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/download\/31827\/33994","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/download\/31827\/33994","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T11:45:28Z","timestamp":1731066328000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/view\/31827"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,8]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,11,8]]}},"URL":"https:\/\/doi.org\/10.1609\/aaaiss.v4i1.31827","relation":{},"ISSN":["2994-4317"],"issn-type":[{"value":"2994-4317","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,8]]}}}