In this study, we explore the impact of different masking strategies on time
series imputation models. We evaluate the effects of pre-masking versus
in-mini-batch masking, normalization timing, and the choice between augmenting
and overlaying artificial missingness. Using three diverse datasets, we
benchmark eleven imputation models with different missing rates. Our results
demonstrate that masking strategies significantly influence imputation
accuracy, revealing that more sophisticated and data-driven masking designs are
essential for robust model evaluation. We advocate for refined experimental
designs and comprehensive disclosureto better simulate real-world patterns,
enhancing the practical applicability of imputation models.

本研究探讨不同的遮蔽策略对时间序列插补模型的影响，评估了预遮蔽与 mini-batch 中的遮蔽、标准化时机以及增强人工缺失和叠加人工缺失之间的选择对插补准确性的影响。使用三个不同的数据集，我们对 11 个插补模型进行了基准测试，其中包含不同的缺失率。我们的结果表明，遮蔽策略显著影响插补准确性，揭示了更复杂和数据驱动的遮蔽设计对于稳健的模型评估至关重要。我们倡导改进的实验设计和全面披露，以更好地模拟现实世界的模式，增强插补模型的实际适用性。