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Missing Value Imputation on Multidimensional Time Series

Summary: DeepMVI is a deep-learning imputation method for multidimensional time series, modeling missing values from temporal signals and cross-series correlations. Robust training with synthetic missing blocks and a temporal-transformer plus learned-embedding kernel regression yields large accuracy gains (often >50%), with slower runtimes but improved downstream analytics. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12427
Venue
VLDB
Year
2021
Pagerank
6.2805243e-05
Overall Rank
4,332 | 69.87%
DOI
10.14778/3476249.3476300

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