用扩散模型修复时间序列缺失值，解决边界不连续和长程依赖问题。
Boundary-enhanced time series data imputation with long-term dependency diffusion models
- 在反向扩散过程中逐步注入预测值，权重递减缓解边界断裂
- 多尺度S4-U-Net融合多分辨率信息，捕捉长期依赖关系
- 在真实医疗与交通数据上优于现有方法，尤其处理长间隔缺失
数据填补对多变量时间序列中缺失值的挑战至关重要，广泛应用于医疗、交通和经济等领域。基于扩散模型的方法表现出显著性能提升，但现有方法常导致缺失与已知区域间边界不协调，并忽略缺失数据中的长程依赖，影响结果质量。为此，本文提出一种基于扩散模型的时间序列数据填补框架（DSDI）。设计权重递减注入策略，将缺失点预测值以逐渐降低的权重引入反向扩散过程，缓解边界不一致问题。进一步构建多尺度S4-U-Net结构，通过多分辨率融合机制整合不同层级的层次化信息，有效捕捉长程依赖。实验表明，所提模型在多个真实数据集上优于现有方法，尤其在长间隔缺失场景下表现更优。
原文摘要 · Abstract (English)
Data imputation is crucial for addressing challenges posed by missing values in multivariate time series data across various fields, such as healthcare, traffic, and economics, and has garnered significant attention. Among various methods, diffusion model-based approaches show notable performance improvements. However, existing methods often cause disharmonious boundaries between missing and known regions and overlook long-range dependencies in missing data estimation, leading to suboptimal results. To address these issues, we propose a Diffusion-based time Series Data Imputation (DSDI) framework. We develop a weight-reducing injection strategy that incorporates the predicted values of missing points with reducing weights into the reverse diffusion process to mitigate boundary inconsistencies. Further, we introduce a multi-scale S4-based U-Net, which combines hierarchical information from different levels via multi-resolution integration to capture long-term dependencies. Experimental results demonstrate that our model outperforms existing imputation methods.
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