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. We propose the Distributionally Robust Regularized Imputer Objective (DRIO), which jointly minimizes reconstruction error and the worst-case divergence between the imputer distribution and data distributions within a Wasserstein ambiguity set. We derive a tractable upper-bound surrogate that reduces infinite-dimensional optimization over measures to adversarial search over sample trajectories, and develop an alternating learning algorithm compatible with modern deep learning backbones. Comprehensive experiments on diverse real-world datasets show that DRIO consistently provides robust imputation and suggests improved downstream forecasting under various missingness scenarios.

翻译：多元时间序列插补常因观测分布与真实数据分布不匹配而效果不佳，这种偏差源于时间序列的非平稳性和系统性缺失的综合效应。鼓励逐点重构或直接分布对齐的标准方法可能过度拟合这些有偏观测。我们提出分布鲁棒正则化插补目标（DRIO），该目标联合最小化重构误差与插补器分布和Wasserstein模糊集内数据分布之间的最坏情况散度。我们推导出一个可处理的代理上界，将测度空间上的无限维优化简化为样本轨迹上的对抗搜索，并开发出与当代深度学习骨干兼容的交替学习算法。在多种真实世界数据集上的综合实验表明，DRIO在各种缺失场景下均能提供鲁棒的插补效果，并展现出对下游预测任务的改进潜力。