Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion

TL;DR

This paper introduces CoFILL, a novel diffusion-based model for spatiotemporal data imputation that effectively captures complex dependencies and outperforms existing methods in accuracy.

Contribution

The paper presents a new dual-stream diffusion model, CoFILL, which processes spatial and temporal features in parallel for improved data imputation.

Findings

CoFILL outperforms state-of-the-art imputation methods.

The model effectively captures both rapid fluctuations and underlying patterns.

Experimental results demonstrate high-quality, accurate imputations.

Abstract

Missing data in spatiotemporal systems presents a significant challenge for modern applications, ranging from environmental monitoring to urban traffic management. The integrity of spatiotemporal data often deteriorates due to hardware malfunctions and software failures in real-world deployments. Current approaches based on machine learning and deep learning struggle to model the intricate interdependencies between spatial and temporal dimensions effectively and, more importantly, suffer from cumulative errors during the data imputation process, which propagate and amplify through iterations. To address these limitations, we propose CoFILL, a novel Conditional Diffusion Model for spatiotemporal data imputation. CoFILL builds on the inherent advantages of diffusion models to generate high-quality imputations without relying on potentially error-prone prior estimates. It incorporates an innovative dual-stream architecture that processes temporal and frequency domain features in parallel. By fusing these complementary features, CoFILL captures both rapid fluctuations and underlying patterns in the data, which enables more robust imputation. The extensive experiments reveal that CoFILL's noise prediction network successfully transforms random noise into meaningful values that align with the true data distribution. The results also show that CoFILL outperforms state-of-the-art methods in imputation accuracy. The source code is publicly available at https://github.com/joyHJL/CoFILL.