Deep learning based forecasting methods have become the methods of choice in many
applications of time series prediction or forecasting often outperforming other approaches …

Learning-based control aims to construct models of a system to use for planning or trajectory
optimization, eg in model-based reinforcement learning. In order to obtain guarantees of …

Traditional sentiment construction in finance relies heavily on the dictionary-based
approach, with a few exceptions using simple machine learning techniques such as Naive …

Quantile regression is widely applied in various fields such as economy, energy,
meteorological prediction research in recent years since it does not require distribution …

This paper develops a conditional quantile model that can learn long term and short term
memories of sequential data. It builds on sequential neural networks and yet outputs …

This article presents a new method for forecasting Value at Risk. Convolutional neural
networks can do time series forecasting, since they can learn local patterns in time. A simple …

In this study, we propose a semiparametric, parsimonious value-at-risk forecasting model,
based on quantile regression and machine learning methods, combined with readily …

Spatial-temporal wind power prediction is of enormous importance to the grid-connected
operation of multiple wind farms in the wind power system. However, most of the …

We propose a novel probabilistic model to facilitate the learning of multivariate tail
dependence of multiple financial assets. Our method allows one to construct from known …

Recent advances in deep learning have shown that uncertainty estimation is becoming
increasingly important in applications such as medical imaging, natural language …