  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.
