Temporal Disentangled Contrastive Diffusion Model for Spatiotemporal Imputation

arXiv:2402.11558v1 Announce Type: new
Abstract: 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. …

abstract analysis arxiv cs.lg data data analysis dependencies diffusion diffusion model domains errors healthcare imputation meteorology missing values network pivotal sensor spatial temporal transportation type values world