发表机构
London Business School; RWTH Aachen University; The Chinese University of Hong Kong; University of Technology Sydney（伦敦商学院; 亚琛工业大学; 香港中文大学; 悉尼科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出无需重新训练的KQSP方法，解决概率K线预测的分位数与K线交叉问题，保持预测准确性并将交叉率降至零。
AI 中文摘要
概率K线预测描述开盘价、最高价、最低价、收盘价（OHLC）这四个互补价格的不确定性，但存在分位数交叉和K线交叉两个一致性问题：分位数交叉指高分位数预测低于低分位数预测，K线交叉指预测最低价超过开盘价或收盘价，或预测最高价低于开盘价。现有解决方案通常仅通过输出重排序、专用架构或惩罚性训练目标解决其中一个问题。我们提出K线-分位数顺序投影（KQSP），这是一种无参数、无需训练的协调方法，适用于任何模型生成的预测。与其他交叉解决方案相比，KQSP在保持预测准确性的同时，对原始预测的修正幅度显著更小。为缓解模型偏差，我们使用包括预训练基础模型在内的多种模型评估KQSP。KQSP对所有测试数据将分位数和K线交叉率降至零。这些结果表明，可独立于预测生成过程且无需重新训练来实现概率K线的一致性。
英文摘要
Probabilistic K-line forecasting describes uncertainty in four complementary prices, namely open--high--low--close (OHLC). However, it introduces two consistency problems: quantile crossing and K-line crossing. Quantile crossing occurs when a higher-quantile forecast falls below a lower-quantile forecast, while K-line crossing occurs when the forecast low exceeds the open or close, or the forecast high falls below the open or close. Existing solutions generally address only one problem through output reordering, specialized architectures, or penalized training objectives. We propose K-line--Quantile Sequential Projection (KQSP), a parameter-free and training-free reconciliation method applicable to forecasts produced by any model. Compared with other crossing solutions, KQSP preserves predictive accuracy while producing substantially smaller corrections to the original forecasts. To mitigate model bias, we evaluate KQSP using various models, including pretrained foundation models. KQSP reduces both quantile and K-line crossing rates to zero for all test data undertaken. These results show that probabilistic K-line consistency can be enforced independently of forecast generation and without retraining.
Comments8 pages, 2 figures, 6 tables
