ReModels: Quantile Regression Averaging models

被引：0
作者：
Zakrzewski, Grzegorz [1 ]
Skonieczka, Kacper [1 ]
Malkinski, Mikolaj [1 ]
Mandziuk, Jacek [1 ,2 ]
机构：
[1] Warsaw Univ Technol, Fac Math & Informat Sci, Warsaw, Poland
[2] AGH Univ Krakow, Fac Comp Sci, Krakow, Poland
来源：
SOFTWAREX | 2024年 / 28卷
关键词：
Machine learning; Energy price forecasting; Probabilistic forecasting; Quantile regression; Quantile regression averaging; QRA; NEURAL-NETWORK;
D O I：
10.1016/j.softx.2024.101905
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Electricity price forecasts are essential for making informed business decisions within the electricity markets. Probabilistic forecasts, which provide a range of possible future prices rather than a single estimate, particularly valuable for capturing market uncertainties. The Quantile Regression Averaging (QRA) method is a leading approach to generating these probabilistic forecasts. In this paper, we introduce ReModels, comprehensive Python package that implements QRA and its various modifications from recent literature. package not only offers tools for QRA but also includes features for data acquisition, preparation, and variance stabilizing transformations (VSTs). To the best of our knowledge, there is no publicly available implementation of QRA and its variants. Our package aims to fill this gap, providing researchers and practitioners with tools to generate accurate and reliable probabilistic forecasts in the field of electricity price forecasting.
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页数：8
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共 29 条
  • [11] Kupiec P., 1995, J. Deriv., V3, P73, DOI 10.3905/jod.1995.407942
  • [12] Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark
    Lago, Jesus
    Marcjasz, Grzegorz
    De Schutter, Bart
    Weron, Rafal
    [J]. APPLIED ENERGY, 2021, 293
  • [13] Probabilistic Load Forecasting via Quantile Regression Averaging on Sister Forecasts
    Liu, Bidong
    Nowotarski, Jakub
    Hong, Tao
    Weron, Rafal
    [J]. IEEE TRANSACTIONS ON SMART GRID, 2017, 8 (02) : 730 - 737
  • [14] Maciejowska K, 2023, Arxiv, DOI [arXiv:2303.08565, 10.1016/j.epsr.2024.110541,110541, DOI 10.1016/J.EPSR.2024.110541,110541]
  • [15] Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging
    Maciejowska, Katarzyna
    Nowotarski, Jakub
    Weron, Rafal
    [J]. INTERNATIONAL JOURNAL OF FORECASTING, 2016, 32 (03) : 957 - 965
  • [16] Distributional neural networks for electricity price forecasting
    Marcjasz, Grzegorz
    Narajewski, Michal
    Weron, Rafal
    Ziel, Florian
    [J]. ENERGY ECONOMICS, 2023, 125
  • [17] Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?
    Marcjasz, Grzegorz
    Uniejewski, Bartosz
    Weron, Rafal
    [J]. INTERNATIONAL JOURNAL OF FORECASTING, 2020, 36 (02) : 466 - 479
  • [18] Recent advances in electricity price forecasting: A review of probabilistic forecasting
    Nowotarski, Jakub
    Weron, Rafal
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2018, 81 : 1548 - 1568
  • [19] Computing electricity spot price prediction intervals using quantile regression and forecast averaging
    Nowotarski, Jakub
    Weron, Rafa
    [J]. COMPUTATIONAL STATISTICS, 2015, 30 (03) : 791 - 803
  • [20] Averaging Predictive Distributions Across Calibration Windows for Day-Ahead Electricity Price Forecasting
    Serafin, Tomasz
    Uniejewski, Bartosz
    Weron, Rafal
    [J]. ENERGIES, 2019, 12 (13)
123