English(EN) Temporal Kolmogorov-Arnold Networks (T-KAN) for High-Frequency Limit Order Book Forecasting: Efficiency, Interpretability, and Alpha Decay

新的 T-KAN 架构提升高频交易预测能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员推出了一种新颖的架构——时间柯尔莫哥洛夫-阿诺德网络 (T-KAN)，旨在改进高频交易 (HFT) 预测。与难以处理嘈杂、非线性限价订单簿数据和 Alpha 衰减的传统模型不同，T-KAN 利用可学习的 B 样条激活函数来捕捉信号形状而非仅是幅度。该方法在 k=100 的预测范围内，F1 分数相对提高了 19.1%，并实现了 132.48% 的回报，而 DeepLOB 则出现了显著回撤。T-KAN 模型还提供了增强的可解释性，并针对低延迟 FPGA 实现进行了优化。 AI

影响有潜力显著提高高频交易环境中的预测准确性和盈利能力。

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arXiv cs.LG TIER_1 English(EN) · Ahmad Makinde · 2026-09-10 04:00

面向高频限价订单簿预测的时序Kolmogorov-Arnold网络（T-KAN）：效率、可解释性与Alpha衰减

arXiv:2601.02310v2 Announce Type: replace Abstract: High-Frequency trading (HFT) environments are characterised by large volumes of limit order book (LOB) data, which is notoriously noisy and non-linear. Alpha decay represents a significant challenge, with traditional models such…

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报道来源 [1]

面向高频限价订单簿预测的时序Kolmogorov-Arnold网络（T-KAN）：效率、可解释性与Alpha衰减

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