arXiv:2506.08026v1 Announce Type: new
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.
Expert Analysis: TIP-Search for Real-Time Market Prediction
In the world of high-frequency financial systems, where split-second decisions can make or break deals, the need for accurate and timely market predictions is paramount. The paper presents a novel solution, TIP-Search, which tackles this challenge by dynamically selecting deep learning models to maximize predictive accuracy while meeting strict latency constraints.
What sets TIP-Search apart is its focus on time predictability, making it well-suited for real-time applications where deadlines must be met consistently. By profiling latency and generalization performance offline, TIP-Search is able to adapt to evolving workloads in real-time without relying on explicit input domain labels.
The multi-disciplinary nature of this framework is worth noting, as it combines principles from machine learning, finance, and real-time systems to address a complex problem in a holistic manner. By evaluating TIP-Search on real-world limit order book datasets from different sources, the paper demonstrates its superiority over static baselines, achieving significant improvements in both accuracy and deadline satisfaction.
Future Implications and Next Steps
As the financial industry continues to embrace automation and algorithmic trading, the demand for efficient and reliable market prediction tools will only grow. TIP-Search’s success in this domain opens up new possibilities for applying similar adaptive scheduling techniques in other time-critical applications beyond finance.
Future research could explore the scalability of TIP-Search to handle larger and more diverse datasets, as well as investigate its performance in scenarios with varying levels of uncertainty. Additionally, integrating TIP-Search with emerging technologies like blockchain and edge computing could further enhance its capabilities in handling real-time market prediction tasks.
Overall, TIP-Search represents a promising step towards achieving robust and low-latency financial inference under uncertain conditions, showcasing the value of integrating machine learning with real-time systems for time-critical applications.