Predictions and forecasts of machine learning models should take the form of probability
distributions, aiming to increase the quantity of information communicated to end users …

Accurate time-series forecasting during high variance segments (eg, holidays), is critical for
anomaly detection, optimal resource allocation, budget planning and other related tasks. At …

Probabilistic forecasts in the form of probability distributions over future events have become
popular in several fields including meteorology, hydrology, economics, and demography. In …

The intermittent nature of renewable resources like solar and wind presents challenges for
small-scale energy markets and off-grid regions. Localized forecasting of these resources is …

Ensembles used for probabilistic weather forecasting tend to be biased and
underdispersive. This paper proposes a statistical method for postprocessing ensembles …

We present a study on the evolution of publications about applications of proper scoring
rules. Specifically, we consider articles reporting the use of proper scoring rules when either …

The continuous ranked probability score (CRPS) is a much used measure of performance
for probabilistic forecasts of a scalar observation. It is a quadratic measure of the difference …

Statistical post‐processing of dynamical forecast ensembles is an essential component of
weather forecasting. In this article, we present a post‐processing method which generates …

Wind turbines (WTs) are designed to operate under extreme environmental conditions. This
means that extreme and varying loads experienced by WT components need to be …

Quantile regression is an effective technique to quantify uncertainty, fit challenging
underlying distributions, and often provide full probabilistic predictions through joint …