nabqr: Python package for improving probabilistic forecasts

Bastian Schmidt Jørgensena, Jan Kloppenborg Møller, Peter Nystrup, Henrik Madsen
1/29/2025
cs.LGstat.APstat.CO

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Abstract

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.

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Cite this paper

@article{jrgensena2025nabqr,
  title  = {nabqr: Python package for improving probabilistic forecasts},
  author = {Bastian Schmidt Jørgensena and Jan Kloppenborg Møller and Peter Nystrup and Henrik Madsen},
  year   = {2025},
  eprint = {2501.17604},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.17604v1}
}

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