Metraipolis › archive › dc_ff161f078922

UQ-LOB: Uncertainty-Aware Limit Order Book Mid-Price Forecasting — arXiv:2609.31491

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.31491
License (as archived): CC0-1.0
Archived: 2026-10-08T01:38:44.131Z
SHA-256 of the archived text: c65229b089fdd1a9fa9dd5512583935aa733048291d16cc697acf52ce41d25ed
Stable URL of this page: https://metraipolis.com/documento/dc_ff161f078922

How to cite

UQ-LOB: Uncertainty-Aware Limit Order Book Mid-Price Forecasting — arXiv:2609.31491 — arXiv (en), https://arxiv.org/abs/2609.31491 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ff161f078922 (Metraipolis archive dc_ff161f078922, sha256 c65229b089fdd1a9fa9dd5512583935aa733048291d16cc697acf52ce41d25ed)

BibTeX

@misc{dcff161f078922,
  title = {UQ-LOB: Uncertainty-Aware Limit Order Book Mid-Price Forecasting — arXiv:2609.31491},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.31491},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ff161f078922; sha256 c65229b089fdd1a9fa9dd5512583935aa733048291d16cc697acf52ce41d25ed},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.31491. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Forecasting short-horizon mid-price movements from limit order book (LOB) data is central to algorithmic trading, yet most deep LOB forecasters are point predictors: they output a direction or a displacement, but never indicate which of their forecasts can be trusted. We introduce UQ-LOB, a lightweight, encoder-agnostic uncertainty quantification module that attaches to any pretrained LOB encoder and, in the spirit of attentive neural processes, conditions each forecast on a context set of recently completed windows whose outcomes are already realised. The UQ-regression variant outputs a calibrated Gaussian over the future tick displacement, while the UQ-classification variant outputs a categorical distribution over down/up/stationary. Both expose a scalar confidence (predicted signal-to-noise ratio or class probability) that supports selective prediction. On 5.2 billion LOB events across seven cryptocurrency assets and horizons of 5, 10 and 15 seconds, UQ-regression attains near-nominal 68% interval coverage, and restricting to the most confident 10% of predictions raises directional macro F1 by 0.11-0.15 for UQ-regression and 0.05-0.11 for UQ-classification, at every horizon. On large, economically meaningful moves, the tightest confidence tier reaches a directional F1 of 0.88 (down) and 0.83 (up) at the 5-second horizon.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-28 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.31491 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T01:12:14.386Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-28?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).