高效计算时间序列签名，支持CPU/GPU加速和自动微分。
pySigLib -- Fast Signature-Based Computations on CPU and GPU
- 基于PyTorch实现签名与核函数的高性能计算。
- 新梯度算法速度提升至原方法的数倍。
- 适合金融时序建模与大规模生成模型研究者。
签名方法在序列数据机器学习中日益重要，尤其在量化金融的时间序列生成模型中表现突出。然而，现有实现难以应对实际场景中的大规模数据集与长序列需求。本文提出pySigLib，一个高性能的Python库，支持在CPU与GPU上对签名及签名核进行优化计算，并完全兼容PyTorch的自动微分机制。除提供可扩展的软件栈外，还引入一种新型签名核梯度计算方案，可在远低于现有库的运行时间内获得精确梯度。
原文摘要 · Abstract (English)
Signature-based methods have recently gained significant traction in machine learning for sequential data. In particular, signature kernels have emerged as powerful discriminators and training losses for generative models on time-series, notably in quantitative finance. However, existing implementations do not scale to the dataset sizes and sequence lengths encountered in practice. We present pySigLib, a high-performance Python library offering optimised implementations of signatures and signature kernels on CPU and GPU, fully compatible with PyTorch's automatic differentiation. Beyond an efficient software stack for large-scale signature-based computation, we introduce a novel differentiation scheme for signature kernels that delivers accurate gradients at a fraction of the runtime of existing libraries.
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