Progressive Adaptation with Credit-Aware Experts for Online Time Series Forecasting

Online time series forecasting (OTSF) requires continual adaptation to evolving data distributions, while supervision for multi-step forecasts becomes progressively available. Waiting for fully matured feedback delays adaptation, and historical expert credit can become stale as experts continue to evolve. We propose PAVE, a multi-expert framework that organizes online adaptation around feedback maturity and capability validity. As partial feedback arrives, PAVE rapidly adapts expert selection through a feedback-driven online routing correction and local responsibility supervision of a neural router. Once all targets of a forecasting record have matured, PAVE assigns sample-level responsibility from prediction-time expert outputs and validates this historical ownership against the experts' current capabilities. The resulting version-aware evidence determines whether a capability should be preserved, rebased, or recovered through responsibility-weighted subspace protection and targeted replay. Experiments on four multivariate forecasting benchmarks show that PAVE consistently outperforms strong online forecasting baselines, while ablation and diagnostic analyses support the effectiveness of progressive routing, version-aware credit assignment, and preservation–recovery.