当明天成为今天：面向智能体时间序列预测的自进化策略

中文摘要 AI 辅助

智能体时间序列预测关注的是底层机制不断演化的系统，这使得数值模型、推理策略和干预规则的相对有效性随时间而变化。因此，时间序列智能体必须调整其生成的预测以及决定信任哪些组件、如何协调它们的编排策略。部署过程自然为这种适应提供了监督，因为预测期限的推移和实现的目标揭示了早期决策的有效性。在目标观测之前提交所有数值专家预测和候选智能体路径，使得每个实现的结果都能评估整个备选集合，从而提供无需额外标注的延迟反馈。然而，现有的时间序列智能体主要通过预测细化、反思或检索来纳入先前经验，而没有系统地将实现的结果转化为对控制后续起点的联合编排策略的持久更新。为了系统地利用这种延迟反馈，我们引入了TimEvolve，一种冻结骨干的时间序列智能体，它将每个实现的结果转化为对专家信任、智能体路径选择和干预强度的持久联合更新。一个按时间排序的预测、揭示和更新协议将此反馈应用于后续预测。在八个Time-MMD领域上的实验表明，TimEvolve在十五种方法中取得了最佳的平均MSE和MAE排名，并在七个领域中取得了两个指标的最低误差。这些结果证明了从部署期间遇到的未来中学习预测策略的价值。

英文摘要

Agentic time series forecasting concerns systems whose underlying mechanisms evolve, making the relative effectiveness of numerical models, reasoning strategies, and intervention rules inherently time-varying. Consequently, a time series agent must adapt the forecasts it produces and the orchestration policy that determines which components to trust and how to coordinate them. The deployment process naturally provides supervision for this adaptation as forecast horizons elapse and realized targets reveal the effectiveness of earlier decisions. Committing all numerical expert forecasts and candidate agent paths before target observation allows each realized outcome to evaluate the entire alternative set, providing delayed feedback without additional annotation. However, existing time series agents primarily incorporate prior experience through forecast refinement, reflection, or retrieval, without systematically converting realized outcomes into persistent updates to the joint orchestration policy governing later origins. To exploit this delayed feedback systematically, we introduce TimEvolve, a frozen-backbone time series agent that converts each realized outcome into persistent joint updates of expert trust, agent path selection, and intervention strength. A temporally ordered predict, reveal, and update protocol applies this feedback to subsequent forecasts. Experiments across eight Time-MMD domains show that TimEvolve achieves the best average MSE and MAE ranks among fifteen methods and the lowest errors on both metrics in seven domains. These results demonstrate the value of learning forecasting policies from the futures encountered during deployment.

发表机构

• Ant International（蚂蚁国际）
• Tsinghua University（清华大学）
• The Chinese University of Hong Kong（香港中文大学）

机构由 AI 辅助整理，请以论文原文为准。