PAST: A Primary-Auxiliary Spatio-Temporal Network for Traffic Time Series Imputation

Traffic time series imputation is vital for transportation reliability, yet diverse missing patterns–including random, fiber, and block missing–pose significant challenges. Existing models typically disentangle spatial and temporal dependencies based on internal relationships but struggle to adapt to stochastic missing positions or capture the long-term and large-scale dependencies required for extensive missingness. We propose the Primary-Auxiliary Spatio-Temporal network (PAST), which categorizes data patterns into primary patterns (derived from internal relationships) and auxiliary patterns (driven by external factors like timestamps and node attributes). PAST employs a Graph-Integrated Module (GIM) to capture primary patterns via dynamic graphs with an interval-aware dropout mechanism, and a Cross-Gated Module (CGM) to extract auxiliary patterns through bidirectional gating. These modules interact via shared hidden vectors under an ensemble self-supervised framework. Experiments on three datasets across 27 missing conditions demonstrate that PAST consistently outperforms seven state-of-the-art baselines, achieving accuracy improvements of up to 26.2% in RMSE.