TIP-Search: 不确定负载下市场预测的时间可预测推理调度
TIP-Search: Time-Predictable Inference Scheduling for Market Prediction under Uncertain Load
AI总结：
提出TIP-Search框架，通过保形延迟分位数过滤、有限工作者调度和带约束在线专家选择，在不确定负载下实现市场预测的时间可预测推理，显著提升及时准确率和截止时间满足率。
摘要：
实时市场预测服务需要在决策截止时间前给出正确预测；延迟的正确预测不是可用的服务输出。TIP-Search研究在不确定负载下对固定市场预测器进行时间可预测的推理调度。它过滤保形延迟分位数可行模型，在有限工作者上调度，并使用带屏蔽的约束在线专家来权衡准确性、队列压力和截止时间风险。官方系统重放控制器是OCO-ACPO，一种投影对偶屏蔽专家选择器；SA-OCO-ACPO是非平稳压力扩展，记录区间压力、遗憾代理和约束违反代理，同时保留CPO安全屏蔽。在优化可部署池上，TIP-Search达到0.994原始准确率和0.991及时准确率。在官方TLOB FI-2010 h=10上，TIP-Search++将及时准确率从0.156提高到0.239，截止时间满足率从0.391提高到0.962。在匹配的h10分析系统重放中，OCO-ACPO达到0.303及时准确率和0.951截止时间满足率，与RAMSIS/SneakPeek/utility风格比较器相比，配对条件增益为$+0.00285$及时准确率（$p=0.0118$）和$+0.0146$截止时间满足率（$p=1.5{\ imes}10^{-5}$）。SA-OCO-ACPO在非平稳压力下将及时/截止服务比CPO提高0.188--0.417。本文声称的是系统调度结果，而非广泛的LOB分类器排行榜。
英文摘要：
Real-time market prediction services need correct predictions before a decision deadline; a correct prediction delivered late is not usable. TIP-Search studies time-predictable inference scheduling over fixed market predictors under uncertain load. It filters conformal latency-quantile feasible models, dispatches over finite workers, and uses shielded constrained online experts to trade accuracy, queue pressure, and deadline risk. On the optimized deployable pool, TIP-Search reaches 0.994 raw accuracy and 0.991 timely accuracy. On official TLOB FI-2010 h=10, TIP-Search++ raises timely accuracy from 0.156 to 0.239 and deadline satisfaction from 0.391 to 0.962. In matched h10 profiled systems replay, OCO-ACPO reaches 0.303 timely accuracy and 0.951 deadline satisfaction, with paired gains over RAMSIS/SneakPeek/utility-style comparators of $+0.00285$ timely accuracy ($p=0.0118$) and $+0.0146$ deadline satisfaction ($p=1.5{\times}10^{-5}$). SA-OCO-ACPO improves timely/deadline service by 0.188--0.417 over CPO under nonstationary stress. The claim is a systems scheduling result, not a broad LOB classifier leaderboard.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
