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 …
Addressing COVID-19 outliers in BVARs with stochastic volatility
The COVID-19 pandemic has led to enormous data movements that strongly affect
parameters and forecasts from standard Bayesian vector autoregressions (BVARs). To …
parameters and forecasts from standard Bayesian vector autoregressions (BVARs). To …
Evaluating probabilistic forecasts with scoringRules
Probabilistic forecasts in the form of probability distributions over future events have become
popular in several fields including meteorology, hydrology, economics, and demography. In …
popular in several fields including meteorology, hydrology, economics, and demography. In …
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 …
The COVID-19 shock and challenges for inflation modelling
E Bobeica, B Hartwig - International journal of forecasting, 2023 - Elsevier
We document the impact of COVID-19 on inflation modelling within a vector autoregression
(VAR) model and provide guidance for forecasting euro area inflation during the pandemic …
(VAR) model and provide guidance for forecasting euro area inflation during the pandemic …
[HTML][HTML] On the use of distribution-adaptive likelihood functions: Generalized and universal likelihood functions, scoring rules and multi-criteria ranking
This paper is concerned with the formulation of an adequate likelihood function in the
application of Bayesian epistemology to uncertainty quantification of hydrologic models. We …
application of Bayesian epistemology to uncertainty quantification of hydrologic models. We …
Improving flood impact estimations
T Sieg, AH Thieken - Environmental Research Letters, 2022 - iopscience.iop.org
A reliable estimation of flood impacts enables meaningful flood risk management and rapid
assessments of flood impacts shortly after a flood. The flood in 2021 in Central Europe and …
assessments of flood impacts shortly after a flood. The flood in 2021 in Central Europe and …
Constructing density forecasts from quantile regressions: Multimodality in macrofinancial dynamics
Quantile regression methods are increasingly used to forecast tail risks and uncertainties in
macroeconomic outcomes. This paper reconsiders how to construct predictive densities from …
macroeconomic outcomes. This paper reconsiders how to construct predictive densities from …
Combining predictive distributions for the statistical post-processing of ensemble forecasts
Statistical post-processing techniques are now used widely for correcting systematic biases
and errors in the calibration of ensemble forecasts obtained from multiple runs of numerical …
and errors in the calibration of ensemble forecasts obtained from multiple runs of numerical …
Parameterizing Lognormal state space models using moment matching
JW Smith, RQ Thomas, LR Johnson - Environmental and Ecological …, 2023 - Springer
In ecology, it is common for processes to be bounded based on physical constraints of the
system. One common example is the positivity constraint, which applies to phenomena such …
system. One common example is the positivity constraint, which applies to phenomena such …