nabqr: Python package for improving probabilistic forecasts
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866912209340203008 |
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| author | Jørgensena, Bastian Schmidt Møller, Jan Kloppenborg Nystrup, Peter Madsen, Henrik |
| author_facet | Jørgensena, Bastian Schmidt Møller, Jan Kloppenborg Nystrup, Peter Madsen, Henrik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_17604 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | nabqr: Python package for improving probabilistic forecasts Jørgensena, Bastian Schmidt Møller, Jan Kloppenborg Nystrup, Peter Madsen, Henrik Machine Learning Applications Computation 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. |
| title | nabqr: Python package for improving probabilistic forecasts |
| topic | Machine Learning Applications Computation |
| url | https://arxiv.org/abs/2501.17604 |