Temporal Disentangled Contrastive Diffusion Model for Spatiotemporal Imputation

Spatiotemporal data analysis is pivotal across various domains, including transportation, meteorology, and healthcare. However, the data collected in real-world scenarios often suffers incompleteness due to sensor malfunctions and network transmission errors. Spatiotemporal imputation endeavours to predict missing values by exploiting the inherent spatial and temporal dependencies present in the observed data. Traditional approaches, which rely on classical statistical and machine learning techniques, are often inadequate, particularly when the data fails to meet strict distributional assumptions. In contrast, recent deep learning-based methods, leveraging graph and recurrent neural networks, have demonstrated enhanced efficacy. Nonetheless, these approaches are prone to error accumulation. Generative models have been increasingly adopted to circumvent the reliance on potentially inaccurate historical imputed values for future predictions. These models grapple with the challenge of producing unstable results, a particular issue in diffusion-based models. We aim to address these challenges by designing conditional features to guide the generative process and expedite training. Specifically, we introduce C$^2$TSD, a novel approach incorporating trend and seasonal information as conditional features and employing contrastive learning to improve model generalizability. The extensive experiments on three real-world datasets demonstrate the superior performance of C$^2$TSD over various state-of-the-art baselines.