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

Magnus Wiese, Phillip Murray and Ralf Korn

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Abstract: 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.

Date: 2023-07
New Economics Papers: this item is included in nep-big and nep-ecm
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