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

• URL: http://arxiv.org/abs/2506.08026v2
• Date: Mon, 16 Jun 2025 19:58:59 GMT
• Title: TIP-Search: Time-Predictable Inference Scheduling for Market Prediction under Uncertain Load
• Authors: Xibai Wang,
• Abstract summary: TIP-Search is a time-predictable inference scheduling framework for real-time market prediction under uncertain workloads.<n>We evaluate TIP-Search on three real-world limit order book datasets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes TIP-Search, a time-predictable inference scheduling framework for real-time market prediction under uncertain workloads. Motivated by the strict latency demands in high-frequency financial systems, TIP-Search dynamically selects a deep learning model from a heterogeneous pool, aiming to maximize predictive accuracy while satisfying per-task deadline constraints. Our approach profiles latency and generalization performance offline, then performs online task-aware selection without relying on explicit input domain labels. We evaluate TIP-Search on three real-world limit order book datasets (FI-2010, Binance BTC/USDT, LOBSTER AAPL) and demonstrate that it outperforms static baselines with up to 8.5% improvement in accuracy and 100% deadline satisfaction. Our results highlight the effectiveness of TIP-Search in robust low-latency financial inference under uncertainty.

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