4 papers
Online forecast reconciliation using linear models
Tobias Rønlev-Knudsen, Henrik Madsen, Jan Kloppenborg Møller
We present a framework for online and adaptive forecasting and hierarchical reconciliation using linear regression models. We begin by formalizing hierarchies using graphs, and mot…
evalprob4cast: An R-package for evaluation of ensembles as probabilistic forecasts or event forecasts
Mathias Blicher Bjerregård, Jethro Browell, John Zack +4
For any forecasting application, evaluation of forecasts is an important task. For example, in the field of renewable energy sources there is high variability and uncertainty of po…
nabqr: Python package for improving probabilistic forecasts
Bastian Schmidt Jørgensena, Jan Kloppenborg Møller, Peter Nystrup +1
We introduce the open-source Python package NABQR: Neural Adaptive Basis for (time-adaptive) Quantile Regression that provides reliable probabilistic forecasts. NABQR corrects ense…
Optimal Forecast Reconciliation with Uncertainty Quantification
Jan Kloppenborg Møller, Peter Nystrup, Poul G. Hjorth +1
We propose to estimate the weight matrix used for forecast reconciliation as parameters in a general linear model in order to quantify its uncertainty. This implies that forecast r…