TIP-Search: Time-Predictable Inference Scheduling for Market Prediction under Uncertain Load

[Submitted on 30 May 2025 (v1), last revised 23 Jun 2026 (this version, v4)]

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Abstract: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.

Submission history

From: Xibai Wang [view email]
[v1] Fri, 30 May 2025 14:52:01 UTC (1,809 KB)
[v2] Mon, 16 Jun 2025 19:58:59 UTC (432 KB)
[v3] Sat, 20 Jun 2026 02:34:11 UTC (84 KB)
[v4] Tue, 23 Jun 2026 06:44:47 UTC (161 KB)