Sig-Splines : Universal Approximation and Convex Calibration of Time Series Generative Models

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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Year of publication:	[2023]
Authors:	Wiese, Magnus ; Phillip, Murray ; Korn, Ralf
Publisher:	[S.l.] : SSRN
Subject:	Theorie \| Theory \| Zeitreihenanalyse \| Time series analysis
Description of contents:	Abstract [papers.ssrn.com]

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Extent:	1 Online-Ressource (13 p)
Type of publication:	Book / Working Paper
Language:	English
Notes:	Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments July 18, 2023 erstellt Volltext nicht verfügbar
Classification:	C15 - Statistical Simulation Methods; Monte Carlo Methods ; C45 - Neural Networks and Related Topics ; C5 - Econometric Modeling ; C53 - Forecasting and Other Model Applications ; C6 - Mathematical Methods and Programming ; C63 - Computational Techniques
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10014343773