A review of predictive uncertainty estimation with machine learning
H Tyralis, G Papacharalampous - Artificial Intelligence Review, 2024 - Springer
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 …
distributions, aiming to increase the quantity of information communicated to end users …
A review of probabilistic forecasting and prediction with machine learning
H Tyralis, G Papacharalampous - arXiv preprint arXiv:2209.08307, 2022 - arxiv.org
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 …
distributions, aiming to increase the quantity of information communicated to end users …
Forecaster's dilemma: extreme events and forecast evaluation
In public discussions of the quality of forecasts, attention typically focuses on the predictive
performance in cases of extreme events. However, the restriction of conventional forecast …
performance in cases of extreme events. However, the restriction of conventional forecast …
An empirical review of dynamic extreme value models for forecasting value at risk, expected shortfall and expectile
C Candia, R Herrera - Journal of Empirical Finance, 2024 - Elsevier
This work provides a selective review of the most recent dynamic models based on extreme
value theory, in terms of their ability to forecast financial losses through different risk …
value theory, in terms of their ability to forecast financial losses through different risk …
Hydrological post-processing for predicting extreme quantiles
H Tyralis, G Papacharalampous - Journal of Hydrology, 2023 - Elsevier
Hydrological post-processing using quantile regression algorithms constitutes a prime
means of estimating the uncertainty of hydrological predictions. Nonetheless, conventional …
means of estimating the uncertainty of hydrological predictions. Nonetheless, conventional …
Local scale invariance and robustness of proper scoring rules
Averages of proper scoring rules are often used to rank probabilistic forecasts. In many
cases, the individual terms in these averages are based on observations and forecasts from …
cases, the individual terms in these averages are based on observations and forecasts from …
[PDF][PDF] Scale dependence: Why the average CRPS often is inappropriate for ranking probabilistic forecasts
Averages of proper scoring rules are often used to rank probabilistic forecasts. In many
cases, the individual observations and their predictive distributions in these averages have …
cases, the individual observations and their predictive distributions in these averages have …
Exceedance probability estimation for a quality test consisting of multiple measurements
S Tamminen, I Juutilainen, J Röning - Expert systems with applications, 2013 - Elsevier
The purpose of this study was to develop methods for exceedance probability estimation in
the case of highly scattered measurement sets. The situation may occur when product …
the case of highly scattered measurement sets. The situation may occur when product …
[PDF][PDF] Local scale invariance and
D Bolin, J Wallin - Statistical Science, 2022 - repository.kaust.edu.sa
Averages of proper scoring rules are often used to rank probabilistic forecasts. In many
cases, the individual terms in these averages are based on observations and forecasts from …
cases, the individual terms in these averages are based on observations and forecasts from …
[PDF][PDF] Probabilistic forecasting and comparative model assessment, with focus on extreme events
S Lerch - 2016 - core.ac.uk
Probabilistic forecasts in the form of probability distributions over future quantities or events
allow to quantify the prediction uncertainty and are essential for informed decision making …
allow to quantify the prediction uncertainty and are essential for informed decision making …