Paper ID: 2307.09767

Sig-Splines: universal approximation and convex calibration of time series generative models

Magnus Wiese, Phillip Murray, Ralf Korn

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.

Submitted: Jul 19, 2023