Irregularly sampled time series (ISTS), characterized by non-uniform time intervals with natural missingness, are prevalent in real-world applications. Existing approaches for ISTS modeling primarily rely on observed values to impute unobserved ones or infer latent dynamics. However, these methods overlook a critical source of learning signal: the reconstruction error inherently produced during model training. Such error implicitly reflects how well a model captures the underlying data structure and can serve as an informative proxy for unobserved values. To exploit this insight, we propose iTimER, a simple yet effective self-supervised pre-training framework for ISTS representation learning. iTimER models the distribution of reconstruction errors over observed values and generates pseudo-observations for unobserved timestamps through a mixup strategy between sampled errors and the last available observations. This transforms unobserved timestamps into noise-aware training targets, enabling meaningful reconstruction signals. A Wasserstein metric aligns reconstruction error distributions between observed and pseudo-observed regions, while a contrastive learning objective enhances the discriminability of learned representations. Extensive experiments on classification, interpolation, and forecasting tasks demonstrate that iTimER consistently outperforms state-of-the-art methods under the ISTS setting.

翻译：不规则采样时间序列（ISTS）以非均匀时间间隔和自然缺失为特征，在现实应用中普遍存在。现有的ISTS建模方法主要依赖观测值来填补未观测值或推断潜在动态。然而，这些方法忽略了一个关键的学习信号来源：模型训练过程中固有产生的重构误差。此类误差隐含反映了模型捕捉底层数据结构的能力，并可作为未观测值的信息化代理。为利用这一洞见，我们提出了iTimER，一个简单而有效的自监督预训练框架，用于ISTS表示学习。iTimER对观测值的重构误差分布进行建模，并通过采样误差与最近可用观测值之间的混合策略，为未观测时间戳生成伪观测值。这将未观测时间戳转化为噪声感知的训练目标，从而产生有意义的重构信号。Wasserstein度量对齐观测区域与伪观测区域的重构误差分布，同时对比学习目标增强了所学表示的区分能力。在分类、插值和预测任务上的大量实验表明，iTimER在ISTS设置下持续优于现有最先进方法。