UQ-LOB: Uncertainty-Aware Limit Order Book Mid-Price Forecasting

arXiv:2609.31491 · cs.AI · Submitted 2026-09-25 · Read on arXiv

cs.AI

Submitted: 2026-09-25

Updated: 2026-09-28

Terminology

• Calibration — 15× in this paper · explained in 7 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Transformer — 4× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Distribution Shift — 2× in this paper · explained in 6 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Uncertainty Quantification — 2× in this paper · explained in 8 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Aleatoric Uncertainty — 1× in this paper · explained in 5 episodes
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Conformal Prediction — 1× in this paper · explained in 5 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Empirical Coverage — 1× in this paper · explained in 2 episodes
• Epistemic Uncertainty — 1× in this paper · explained in 10 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Neural Processes
• A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
• LOBERT: Generative AI Foundation Model for Limit Order Book Messages
• DeepFolio: Convolutional Neural Networks for Portfolios with Limit Order Book Data
• On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks
• Transformers for Limit Order Books
• BDLOB: Bayesian Deep Convolutional Neural Networks for Limit Order Books
• Extending Deep Learning Models for Limit Order Books to Quantile Regression
• The BerHu penalty and the grouped effect

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