用可学习的B样条函数替代LSTM固定权重，提升高频订单簿预测精度与可解释性。
Temporal Kolmogorov-Arnold Networks (T-KAN) for High-Frequency Limit Order Book Forecasting: Efficiency, Interpretability, and Alpha Decay
- 以B样条激活函数取代LSTM线性权重，捕捉市场信号形状变化。
- 在k=100时F1-score相对提升19.1%，交易成本1.0 bps下收益达132.48%。
- 模型具备清晰可解释的'死区'特征，适合低延迟FPGA部署。
高频交易环境中的限价订单簿（LOB）数据量大、噪声高且非线性强。传统模型如DeepLOB随预测时长（k）增加而出现显著的阿尔法衰减。本文基于FI-2010数据集，提出时序柯尔莫哥洛夫-阿诺德网络（T-KAN），将标准LSTM中固定的线性权重替换为可学习的B样条激活函数，使模型能学习市场信号的‘形状’而非仅其幅度。该方法在k=100时实现F1-score相对提升19.1%。此外，T-KAN在1.0 bps交易成本下实现132.48%收益率，远超DeepLOB的-82.76%回撤。模型还具有良好的可解释性，其‘死区’特征在样条中清晰可见。该架构通过高层次综合（HLS）优化，适用于低延迟FPGA实现。实验代码见：https://github.com/AhmadMak/Temporal-Kolmogorov-Arnold-Networks-T-KAN-for-High-Frequency-Limit-Order-Book-Forecasting。
原文摘要 · Abstract (English)
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 as DeepLOB losing predictive power as the time horizon (k) increases. In this paper, using data from the FI-2010 dataset, we introduce Temporal Kolmogorov-Arnold Networks (T-KAN) to replace the fixed, linear weights of standard LSTMs with learnable B-spline activation functions. This allows the model to learn the 'shape' of market signals as opposed to just their magnitude. This resulted in a 19.1% relative improvement in the F1-score at the k = 100 horizon. The efficacy of T-KAN networks cannot be understated, producing a 132.48% return compared to the -82.76% DeepLOB drawdown under 1.0 bps transaction costs. In addition to this, the T-KAN model proves quite interpretable, with the 'dead-zones' being clearly visible in the splines. The T-KAN architecture is also uniquely optimized for low-latency FPGA implementation via High level Synthesis (HLS). The code for the experiments in this project can be found at https://github.com/AhmadMak/Temporal-Kolmogorov-Arnold-Networks-T-KAN-for-High-Frequency-Limit-Order-Book-Forecasting.
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