AI 中文总结
研究有限时域最优停止问题，提出CC - AOS方法，通过联合摊销拟合反向归纳学习共享延续值模型，确定函数性质并推导误差界限，实验表明该方法在FordA数据集上相比其他方法有优势。
AI 中文摘要
有限时域最优停止是早期时间序列分类中的核心问题，现有数据驱动的反向归纳方法分别求解每个成本 - 时域操作点，效率低下。我们提出CC - AOS，一种针对具有连续成本和多时域的有限时域停止问题的结构化摊销求解器。它通过联合摊销拟合反向归纳学习共享的延续值模型。我们确定了精确值和延续函数的性质并编码在模型架构中，还推导了基于残差的值和策略误差界限。实验表明，在六个未见的FordA成本 - 时域对上，一个CC - AOS检查点在终端风险加采样成本目标上比独立拟合的凸函数学习平均降低15.75%，且平均匹配调谐静态阈值。
英文摘要
Finite-horizon optimal stopping is a central problem in early time-series classification, where a system must decide at each sequence prefix whether the expected benefit of another observation justifies its acquisition cost. Existing data-driven backward-induction methods typically solve each cost-horizon operating point separately, so changing operating conditions requires repeated optimization and separate model stacks, making continuous cost adaptation and multi-horizon deployment inefficient. We propose CC-AOS (Cost- and Horizon-Conditioned Amortized Optimal Stopping), a structured amortized solver for a family of finite-horizon stopping problems with continuous costs and multiple horizons. CC-AOS learns a shared continuation-value model conditioned on the current state, absolute time, remaining horizon, and acquisition cost through joint amortized fitted backward induction. We establish that the exact value and continuation functions are nondecreasing, concave, and horizon-dependently Lipschitz in cost, encode these properties in the model architecture, and derive residual-based bounds on value and policy errors. Experiments on controlled Gaussian and time-varying non-Gaussian processes and the FordA engine-noise time-series benchmark compare CC-AOS with representative per-operating-point backward-induction solvers and tuned static stopping rules. At six unseen FordA cost-horizon pairs, one CC-AOS checkpoint achieved a lower terminal-risk-plus-sampling-cost objective than independently fitted Convex Function Learning at all six pairs, with an average reduction of 15.75 percent, while matching the tuned static thresholds on average.
Comments17 pages, 2 figures, and 4 tables
