ST-Imputer: Multivariate dependency-aware diffusion network with physics guidance for spatiotemporal imputation

Data preparation is crucial for achieving optimal results in deep learning. Unfortunately, missing values are common when preparing large-scale spatiotemporal databases. Most existing imputation methods primarily focus on exploring the spatiotemporal correlations of single-source data; however, high missing rates in single-source data result in sparse distributions. Furthermore, existing methods typically focus on shallow correlations at a single scale, limiting the ability of imputation models to effectively leverage multi-scale spatial features. To tackle these challenges, we propose a multivariate dependency-aware spatiotemporal imputation model, named ST-Imputer. Specifically, we introduce multi-source context data to provide sufficient correlation features for target data (i.e ., data that needs imputation), alleviating the issue of insufficient available features caused by high missing rates in single-source data. By applying a multi-variate spatiotemporal dependency extraction module, ST-Imputer captures potential associations between different spatial scales. Subsequently, the noise prediction module utilizes the learned dual-view features to formulate the spatiotemporal transmission module, thereby reducing weight errors caused by excessive noise. Finally, physical constraints are applied to prevent unrealistic predictions. Extensive experiments on three large-scale datasets demonstrate the significant superiority of ST-Imputer, achieving up to a 13.07 % improvement in RMSE. The code of our model is available at https://github.com/Lion1a/ST-Imputer .