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Sci."],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Multivariate time series (MTS) data are vital for various applications, particularly in machine learning tasks. However, challenges such as sensor failures can result in irregular and misaligned data with missing values, thereby complicating their analysis. While recent advancements use graph neural networks (GNNs) to manage these Irregular Multivariate Time Series (IMTS) data, they generally require a reliable graph structure, either pre-existing or inferred from adequate data to properly capture node correlations. This poses a challenge in applications where IMTS data are often streamed and waiting for future data to estimate a suitable graph structure becomes impractical. To overcome this, we introduce a dynamic GNN model suited for streaming characteristics of IMTS data, incorporating an instance-attention mechanism that dynamically learns and updates graph edge weights for realtime analysis. We also tailor strategies for high-frequency and low-frequency data to enhance prediction accuracy. Empirical results on real-world datasets demonstrate the superiority of our proposed model in both classification and imputation tasks.<\/jats:p>","DOI":"10.1007\/s11704-024-40449-z","type":"journal-article","created":{"date-parts":[[2025,1,12]],"date-time":"2025-01-12T21:15:09Z","timestamp":1736716509000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Adapting to the stream: an instance-attention GNN method for irregular multivariate time series data"],"prefix":"10.1007","volume":"19","author":[{"given":"Kun","family":"Han","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abigail M. Y.","family":"Koay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan K. 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