Deep Generative Quantile-Copula Models for Probabilistic Forecasting
arXiv:1907.10697
Abstract
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.
arXiv commentPublished at the 36th International Conference on Machine Learning (ICML2019), Time Series Workshop, Long Beach, California, 2019
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Cited by in corpus (5)
- Time Series Forecasting With Deep Learning: A Survey
- Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
- Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting
- The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models
- MQTransformer: Multi-Horizon Forecasts with Context Dependent and Feedback-Aware Attention