用两阶段扩散模型重建缺失的时空数据，提升精度与细节。
From Incomplete Coarse-Grained to Complete Fine-Grained: A Two-Stage Framework for Spatiotemporal Data Reconstruction
- 分两阶段：先用扩散模型补空间信息，再用时间模式网络补时序细节。
- 在多个真实数据集上优于现有方法，完整数据重建准确率显著提升。
- 适合传感器数据稀疏、隐私受限场景，如城市交通与环境监测。
随着各类传感设备的快速发展，时空数据日益重要。但由于传感成本和隐私考虑，采集的数据往往不完整且粒度粗，限制了其在特定任务中的应用。为此，我们提出一项新任务——时空数据重建，旨在从稀疏、粗粒度观测中推断出完整、细粒度的数据。为此，我们引入一个基于去噪扩散概率模型（DDPM）的两阶段数据推理框架DiffRecon。第一阶段提出Diffusion-C，通过ST-PointFormer这一强大编码器利用稀疏数据点间的空间相关性；第二阶段引入Diffusion-F，结合提出的T-PatternNet捕捉序列数据中的时间模式。两个阶段共同构成端到端框架，可从不完整、粗粒度观测中推断出完整、细粒度数据。我们在多个真实世界数据集上进行了实验，验证了该方法的优越性。
原文摘要 · Abstract (English)
With the rapid development of various sensing devices, spatiotemporal data is becoming increasingly important nowadays. However, due to sensing costs and privacy concerns, the collected data is often incomplete and coarse-grained, limiting its application to specific tasks. To address this, we propose a new task called spatiotemporal data reconstruction, which aims to infer complete and fine-grained data from sparse and coarse-grained observations. To achieve this, we introduce a two-stage data inference framework, DiffRecon, grounded in the Denoising Diffusion Probabilistic Model (DDPM). In the first stage, we present Diffusion-C, a diffusion model augmented by ST-PointFormer, a powerful encoder designed to leverage the spatial correlations between sparse data points. Following this, the second stage introduces Diffusion-F, which incorporates the proposed T-PatternNet to capture the temporal pattern within sequential data. Together, these two stages form an end-to-end framework capable of inferring complete, fine-grained data from incomplete and coarse-grained observations. We conducted experiments on multiple real-world datasets to demonstrate the superiority of our method.
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