Multivariate Time Series Data Imputation via Distributionally Robust Regularization

ArXi:2602.00844v2 Announce Type: replace-cross Multivariate time series imputation is often compromised by mismatch between the observed and true data distributions, a bias induced by the combined effects of time-series non-stationarity and systematic missingness. Standard methods that encourage point-wise reconstruction or direct distributional alignment may overfit these biased observations.