UQ-LOB: Uncertainty-Aware Limit Order Book Mid-Price Forecasting· UQ-LOB: 带不确定性的订单簿中价预测
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-n
提出一种带不确定性的订单簿中价预测模型UQ-LOB，提升预测可靠性。
- 核心方法
- UQ-LOB模块可附加到任何预训练的LOB编码器上，通过条件化于已实现结果的最近窗口来量化不确定性。回归版输出未来tick位移的校准高斯分布，分类版输出涨/跌/平稳的分类分布。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 现有的LOB（限价订单簿）预测模型大多为点预测，无法提供预测的可信度。
- 关键实验
- 未提供
- 主要贡献
- 提供了一种轻量级的、与编码器无关的不确定性量化方法，能够为短期中价预测增加可信度指标。
- 意义与局限
- 提高了算法交易中LOB数据预测的实用性与可靠性，但实验部分未提供，效果有待验证。