We propose a novel generative model for multivariate discrete-time time series data. Drawing inspiration from the construction of neural spline flows, our algorithm incorporates linear transformations and the signature transform as a seamless substitution for traditional neural networks. This approach enables us to achieve not only the universality property inherent in neural networks but also introduces convexity in the model's parameters.

翻译：我们提出了一种面向多变量离散时间序列数据的新型生成模型。受神经样条流构建的启发，该算法将线性变换与签名变换作为传统神经网络的天然替代方案。这一方法不仅保持了神经网络固有的通用性特性，还引入了模型参数的凸性。