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Latent-driven Imputation and Forecasting for partially observed Time Series (LIFTS)
Partially observed multivariate time series pose two coupled challenges: recovering missing measurements and forecasting future trajectories when observation patterns are informative. Existing methods often assume ignorable missingness or use the mask only as an auxiliary input. We propose LIFTS (La
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