Learning from Highly Sparse Spatio-temporal Data

Incomplete spatio-temporal data in real-world has spawned many research.However, existing methods often utilize iterative message-passing across temporal and spatial dimensions, resulting in substantial information loss and high computational cost.We provide a theoretical analysis revealing that such iterative models are not only susceptible to data sparsity but also to graph sparsity, causing unstable performances on different datasets.To overcome these limitations, we introduce a novel method named One-step Propagation and Confidence-based Refinement (OPCR).In the first stage, OPCR leverages inherent spatial and temporal relationships by employing sparse attention mechanism.These modules propagate limited observations directly to the global context through one-step imputation, which are theoretically effected only by data sparsity.Following this, we assign confidence levels to the initial imputations by correlating missing data with valid data.This confidence-based propagation refines the seperate spatial and temporal imputation results through spatio-temporal dependencies.We evaluate the proposed model across various downstream tasks involving highly sparse spatio-temporal data.Empirical results indicate that our model outperforms state-of-the-art imputation methods, demonstrating its superior effectiveness and robustness.