  Spatiotemporal data analysis is pivotal across various domains, such as
transportation, meteorology, and healthcare. The data collected in real-world
scenarios are often incomplete due to device malfunctions and network errors.
Spatiotemporal imputation aims to predict missing values by exploiting the
spatial and temporal dependencies in the observed data. Traditional imputation
approaches based on statistical and machine learning techniques require the
data to conform to their distributional assumptions, while graph and recurrent
neural networks are prone to error accumulation problems due to their recurrent
structures. Generative models, especially diffusion models, can potentially
circumvent the reliance on inaccurate, previously imputed values for future
predictions; However, diffusion models still face challenges in generating
stable results. We propose to address these challenges by designing conditional
information to guide the generative process and expedite the training process.
We introduce a conditional diffusion framework called C$^2$TSD, which
incorporates disentangled temporal (trend and seasonality) representations as
conditional information and employs contrastive learning to improve
generalizability. Our extensive experiments on three real-world datasets
demonstrate the superior performance of our approach compared to a number of
state-of-the-art baselines.
