TIP-Search: Time-Predictable Inference Scheduling for Market Prediction under Uncertain Load
arXiv:2506.08026v4 Announce Type: replace 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 TL
The increasing reliance on real-time AI for high-stakes applications like market prediction is driving research into robust scheduling mechanisms to ensure timely and accurate inferences.
Ensuring time-predictable and reliable AI inferences is critical for market stability and the efficacy of automated trading and financial decision-making systems.
This research introduces methods to explicitly manage latency and deadline adherence in AI inference systems, offering a framework for more dependable real-time AI deployments.
- · High-frequency trading firms
- · Financial AI developers
- · AI infrastructure providers
- · Systems with unpredictable AI latencies
- · Manual trading operations
Increased reliability and adoption of AI systems in time-sensitive financial markets.
Potential for new financial products and services built on guaranteed timely AI predictions.
Shift in competitive landscape favoring firms with superior real-time AI deployment capabilities.
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Read at arXiv cs.AI