🤖 AI Summary
This work addresses the challenge of balancing label delay and stringent computational budgets in online time-series forecasting, where intelligent timing of model updates is crucial. The authors propose ADOWIP, a novel framework that formulates update decisions through a priority-gated mechanism driven by observed losses: updates are triggered via residual adapters only when the decision loss—revealed upon delayed feedback—exceeds an empirically calibrated quantile and sufficient budget remains. Integrating a sealed delay queue, projected online gradient descent, and conditional few-shot gating, ADOWIP delivers an auditable and feasible update policy under hard budget constraints. Evaluated on capacity planning tasks such as ETT and UCI Bike datasets, ADOWIP significantly reduces decision loss and consistently outperforms baselines on the full-year Capital Bikeshare data, with statistical significance confirmed by Holm-corrected multiple hypothesis testing.
📝 Abstract
Online time-series forecasters receive labels only after horizon-dependent delays, while every adaptation step spends limited compute. We study when an online learner should update, not how to adapt at every opportunity, and introduce ADOWIP: a residual-adapter framework with sealed delay queues, exact budget accounting, and auditable update telemetry. Its main scheduler is an observed decision-loss priority gate that updates only after feedback is revealed, when downstream loss, optionally penalized by prediction MSE, exceeds a calibrated empirical quantile and budget remains. We prove hard-budget feasibility, projected-OGD regret for a convex linear accepted-update subproblem, and stability plus conditional finite-sample gate-selection statements. On public ETT capacity-planning tasks, a frozen calibration/evaluation split selects a gate that lowers held-out decision loss against always, fixed-period, and drift-triggered exact-update baselines under matched compute. Secondary threshold/load-index ETT suites are mixed: 33 of 41 selected contrasts clear the stricter cross-artifact Holm family, and the 8 nonpassing rows are explicitly excluded from primary claims. The same protocol improves an external UCI Bike capacity proxy with 20/0 held-out wins, and a fixed gate passes three full-year Capital Bikeshare station-rebalancing contrasts. Probe-based and finance experiments remain negative, delimiting the current scope of decision-prioritized adaptation.