Result: nabqr: Python package for improving probabilistic forecasts

Title:
nabqr: Python package for improving probabilistic forecasts
Authors:
Jørgensena, Bastian Schmidt, Møller, Jan Kloppenborg, Nystrup, Peter, Madsen, Henrik
Publication Year:
2025
Collection:
Computer Science
Statistics
Subject Terms:
Computer Science - Machine Learning, Statistics - Applications, Statistics - Computation
Document Type:
Report Working Paper
Access URL:
http://arxiv.org/abs/2501.17604
Accession Number:
edsarx.2501.17604
Database:
arXiv

Further Information

We introduce the open-source Python package NABQR: Neural Adaptive Basis for (time-adaptive) Quantile Regression that provides reliable probabilistic forecasts. NABQR corrects ensembles (scenarios) with LSTM networks and then applies time-adaptive quantile regression to the corrected ensembles to obtain improved and more reliable forecasts. With the suggested package, accuracy improvements of up to 40% in mean absolute terms can be achieved in day-ahead forecasting of onshore and offshore wind power production in Denmark.