Deep Generative Quantile-Copula Models for Probabilistic Forecasting

24 Jul 2019Ruofeng WenKari Torkkola

We introduce a new category of multivariate conditional generative models and demonstrate its performance and versatility in probabilistic time series forecasting and simulation. Specifically, the output of quantile regression networks is expanded from a set of fixed quantiles to the whole Quantile Function by a univariate mapping from a latent uniform distribution to the target distribution... (read more)

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