Missing values are prevalent in multivariate time series, compromising the integrity of analyses and degrading the performance of downstream tasks. Consequently, research has focused on multivariate time series imputation, aiming to accurately impute the missing values based on available observations. A key research question is how to ensure imputation consistency, i.e., intra-consistency between observed and imputed values, and inter-consistency between adjacent windows after imputation. However, previous methods rely solely on the inductive bias of the imputation targets to guide the learning process, ignoring imputation consistency and ultimately resulting in poor performance. Diffusion models, known for their powerful generative abilities, prefer to generate consistent results based on available observations. Therefore, we propose a conditional diffusion model for Multivariate Time Series Consistent Imputation (MTSCI). Specifically, MTSCI employs a contrastive complementary mask to generate dual views during the forward noising process. Then, the intra contrastive loss is calculated to ensure intra-consistency between the imputed and observed values. Meanwhile, MTSCI utilizes a mixup mechanism to incorporate conditional information from adjacent windows during the denoising process, facilitating the inter-consistency between imputed samples. Extensive experiments on multiple real-world datasets demonstrate that our method achieves the state-of-the-art performance on multivariate time series imputation task under different missing scenarios. Code is available at https://github.com/JeremyChou28/MTSCI.

翻译：多元时间序列中普遍存在缺失值，这会损害分析的完整性并降低下游任务的性能。因此，研究聚焦于多元时间序列填补，旨在基于可用观测值准确填补缺失值。一个关键的研究问题是如何确保填补的一致性，即观测值与填补值之间的内部一致性，以及填补后相邻窗口之间的外部一致性。然而，先前的方法仅依赖填补目标的归纳偏置来指导学习过程，忽略了填补一致性，最终导致性能不佳。扩散模型以其强大的生成能力而闻名，更倾向于基于可用观测生成一致的结果。因此，我们提出了一种用于多元时间序列一致性填补（MTSCI）的条件扩散模型。具体而言，MTSCI在前向加噪过程中采用对比互补掩码生成双视图。然后，计算内部对比损失以确保填补值与观测值之间的内部一致性。同时，MTSCI在去噪过程中利用混合机制融入来自相邻窗口的条件信息，以促进填补样本间的外部一致性。在多个真实世界数据集上的大量实验表明，我们的方法在不同缺失场景下的多元时间序列填补任务中实现了最先进的性能。代码可在 https://github.com/JeremyChou28/MTSCI 获取。