Cautious Adaptation under Persistent–Transient Ambiguity for Online Forecasting

Online forecasting methods increasingly emphasize rapid adaptation to concept drift and equate model adaptation with immediate deployment, yet newly observed behavior may indicate either the beginning of a persistent shift or a transient episode caused by temporary events, sensor failure, or malicious poisoning. At the moment the data is received, these alternatives may be indistinguishable, although they imply opposite decisions for future deployment. We therefore formulate online adaptation as an operational deployment decision problem under persistent–transient ambiguity. A newly updated model is promoted only if its out-of-sample forecasts demonstrate statistically credible improvements over the previous model. We prove that any model promotion rule using only update-time information incurs a nonzero worst-case excess risk across indistinguishable persistent and transient continuations. The resulting framework shifts the emphasis from unconditional rapid adaptation toward cautious updating. Our method hedges the frozen models while out-of-sample evidence accumulates during the subsequent interval, then uses a Diebold–Mariano test to determine promotion. We further establish a pathwise within-cycle sublinear regret bound relative to the better frozen model. Experiments on five forecasting benchmarks show that our method is competitive with strong recent online forecasting baselines, while operational diagnostics characterize its deployment behavior beyond predictive MSE.