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arXiv · 2307.09767

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

Magnus Wiese·Phillip Murray·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.

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Magnus Wiese, Phillip Murray, Ralf Korn. 2023-07-19. Sig-Splines: universal approximation and convex calibration of time series generative models. https://arxiv.org/abs/2307.09767

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