Multivariate time-series modeling with generative neural networks

25 Feb 2020 Marius Hofert Avinash Prasad Mu Zhu

Generative moment matching networks (GMMNs) are introduced as dependence models for the joint innovation distribution of multivariate time series (MTS). Following the popular copula-GARCH approach for modeling dependent MTS data, a framework based on a GMMN-GARCH approach is presented... (read more)

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