Time-series models are increasingly deployed in operational systems (e.g., inventory management and clinical care) where predictions trigger interventions that alter future observations, producing records we term AI-shaped trajectories. This sequence-prediction coupling deepens as such systems evolve into agents that autonomously predict, act, and remember across long horizons. Such AI-shaped trajectories support immediate performance monitoring, but turn ambiguous once reused as passive data. An algorithm may then misread a prediction-prevented stockout as naturally stable demand, or an averted medical crisis as a false alarm. This ambiguity then propagates into everything built on the record, from retraining and agent memory to downstream predictive tasks. As a result, systems may internalize these intervention effects as natural phenomena, drifting further from the truth at every closed-loop cycle. In this Blue Sky paper, we advocate an agenda for reflexive time-series analysis that shifts the analytical object from the intervention-agnostic sequence to the prediction-action-observation loop. The agenda requires closed-loop provenance to track the operational context (e.g., predictions, policies, and actions), sequence identification to estimate how those actions affect future values under explicit causal assumptions, and closed-loop evaluation to separate intrinsic process prediction from policy-driven effects. This foundation is essential for integrating autonomous agents into such loops at scale, enabling the joint learning of intervention effects and intrinsic dynamics for robust temporal representations.

翻译：暂无翻译