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

• URL: http://arxiv.org/abs/2307.09767v1
• Date: Wed, 19 Jul 2023 05:58:21 GMT
• Title: Sig-Splines: universal approximation and convex calibration of time series generative models
• Authors: Magnus Wiese, Phillip Murray, Ralf Korn
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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