用一致性模型加速多变量时间序列补全，速度提升98%仍保持高精度。
CoSTI: Consistency Models for (a faster) Spatio-Temporal Imputation
- 基于一致性训练，避免扩散模型的迭代推断
- 在多个数据集上实现与扩散模型相当的补全效果
- 适合实时场景，如医疗监测、交通管理
多变量时间序列补全（MTSI）在医疗监测、交通管理等应用中至关重要，不完整数据会危及决策。现有先进方法如去噪扩散概率模型（DDPMs）虽精度高，但因迭代推理导致计算成本大、耗时长。本文提出CoSTI，首次将一致性模型（CMs）应用于MTSI领域。CoSTI通过一致性训练，在保持与DDPMs相当补全质量的同时，显著降低推理时间。在多个数据集和缺失模式下评估，其补全速度最高可提升98%，且性能与扩散模型持平。该工作弥合了生成式补全任务中效率与精度的鸿沟，为关键时空系统中的缺失数据处理提供了可扩展解决方案。
原文摘要 · Abstract (English)
Multivariate Time Series Imputation (MTSI) is crucial for many applications, such as healthcare monitoring and traffic management, where incomplete data can compromise decision-making. Existing state-of-the-art methods, like Denoising Diffusion Probabilistic Models (DDPMs), achieve high imputation accuracy; however, they suffer from significant computational costs and are notably time-consuming due to their iterative nature. In this work, we propose CoSTI, an innovative adaptation of Consistency Models (CMs) for the MTSI domain. CoSTI employs Consistency Training to achieve comparable imputation quality to DDPMs while drastically reducing inference times, making it more suitable for real-time applications. We evaluate CoSTI across multiple datasets and missing data scenarios, demonstrating up to a 98% reduction in imputation time with performance on par with diffusion-based models. This work bridges the gap between efficiency and accuracy in generative imputation tasks, providing a scalable solution for handling missing data in critical spatio-temporal systems.
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