From Incomplete Coarse-Grained to Complete Fine-Grained: A Two-Stage Framework for Spatiotemporal Data Reconstruction

TL;DR

This paper introduces a two-stage diffusion-based framework, DiffRecon, that reconstructs complete, fine-grained spatiotemporal data from sparse, coarse observations, leveraging spatial and temporal modeling for improved accuracy.

Contribution

The paper presents a novel two-stage diffusion framework, DiffRecon, combining ST-PointFormer and T-PatternNet to effectively infer detailed spatiotemporal data from incomplete inputs.

Findings

Outperforms existing methods on real-world datasets

Effectively captures spatial correlations between data points

Accurately models temporal patterns in sequential data

Abstract

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.