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

Paper: arXiv 2506.08026

Authors: Xibai Wang

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.

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces a formal optimization formulation and a Pareto frontier concept for model selection under constraints, demonstrating moderate mathematical complexity. It provides strong empirical rigor by testing on multiple real-world LOB datasets, implementing dynamic scheduling, and reporting quantitative improvements (e.g., 8.5% accuracy gain, 100% deadline satisfaction) with ablation studies.

Research Flowchart

  flowchart TD
  A["Research Goal: Develop a time-predictable<br>inference scheduler for real-time<br>market prediction under uncertain load"] --> B["Data Preparation: Use Limit Order Book datasets<br>(FI-2010, Binance BTC/USDT, LOBSTER AAPL)"]
  B --> C["Offline Profiling: Characterize model latency<br>and generalization performance"]
  C --> D["Online Scheduling: Dynamic model selection<br>via TIP-Search to maximize accuracy<br>while meeting strict deadlines"]
  D --> E{"Evaluation & Outcomes"}
  E --> F["+8.5% Accuracy<br>vs. static baselines"]
  E --> G["100% Deadline Satisfaction<br>under uncertain load"]
  E --> H["Robust Low-Latency<br>Financial Inference"]

Cited References (15)

1. DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (2019) — link
2. Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods (2018) — link
3. Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment (1973) — link
4. LOBSTER: Limit Order Book Reconstruction System (2011) — link
5. LiT: Limit Order Book Transformer (2025) — link
6. ViT-LOB: Efficient Vision Transformer for StockPrice Trend Prediction Using Limit Order Books (2024) — link
7. TLOB: A Novel Transformer Model with Dual Attention for Stock Price Trend Prediction with Limit Order Book Data (2025) — link
8. Deep Limit Order Book Forecasting — → 2403.09267

1 of 15 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.