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We propose a novel generative model for multivariate discrete-time timeseries data. Drawing inspiration from the construction of neural spline flows,our algorithm incorporates linear transformations and the signature transformas a seamless substitution for traditional neural networks. This approachenables us to achieve not only the universality property inherent in neuralnetworks but also introduces convexity in the model's parameters.

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