DSSER: Dynamic Sparse Spatiotemporal Evidence Restoration for Drift-Robust Multivariate Time Series Forecasting

Multivariate time series forecasting predicts future states from multiple interdependent historical series, yet real-world observations are often incomplete. Existing approaches typically address missingness by reconstructing unavailable values or learning representations robust under incomplete inputs. However, we observe that forecasts from incomplete observations drift away from those obtained with complete histories as missingness conditions vary. This drift arises from missingness-induced changes in the availability of historical evidence and the reliability of the remaining evidence. To mitigate such drift, we propose Dynamic Sparse Spatiotemporal Evidence Restoration (DSSER). The framework selectively leverages reliable evidence available under incomplete observations to improve forecasting stability. Reliability-Guided Evidence Routing (RGER) further regulates the contribution of restored evidence through learned routing confidence, introducing it as a bounded residual correction to the base forecast. To maintain reliability across training and inference, DSSER further introduces Cross-Stage Uncertainty Calibration (CSUC). Teacher predictive uncertainty and branch missingness jointly regulate teacher–student supervision during training, while learned routing confidence regulates restored evidence utilization at inference. Across three public traffic and air-quality benchmarks, DSSER consistently outperforms existing methods under random missingness, achieving the best results across all 36 dataset-rate-metric combinations. Under unseen-rate and dynamic-missing shifts, DSSER maintains robust performance, reducing MAE by 2.5%–3.6% on average relative to the best-performing non-DSSER baselines.