Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …

Probabilistic forecasting is receiving growing attention nowadays in a variety of applied
fields, including hydrology. Several machine learning concepts and methods are notably …

Wind speed forecasting plays an important role in the stable operation of wind energy power
systems. However, accurate and reliable wind speed forecasting faces four challenges: how …

Conformal prediction is a technique for constructing prediction intervals that attain valid
coverage in finite samples, without making distributional assumptions. Despite this appeal …

This paper is derived from a keynote talk given at the Google's 2020 Flood Forecasting
Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall …

We propose a framework for general probabilistic multi-step time series regression.
Specifically, we exploit the expressiveness and temporal nature of Sequence-to-Sequence …

Abstract Conformal inference, cross-validation+, and the jackknife+ are hold-out methods
that can be combined with virtually any machine learning algorithm to construct prediction …

Ensemble weather predictions require statistical postprocessing of systematic errors to
obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with …

We provide single-model estimates of aleatoric and epistemic uncertainty for deep neural
networks. To estimate aleatoric uncertainty, we propose Simultaneous Quantile Regression …

With the increasing utilization of wind generation in power system, the improvement of wind
power forecasting precision is attached vital importance. Owing to the stochastic and …