Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Generative Quantile-Copula Models for Probabilistic Forecasting   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. Then the multivariate case is solved by learning such
quantile functions for each dimension's marginal distribution, followed by
estimating a conditional Copula to associate these latent uniform random
variables. The quantile functions and copula, together defining the joint
predictive distribution, can be parameterized by a single implicit generative
Deep Neural Network.

13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an