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stat.AP2025
Data-driven Calibration Sample Selection and Forecast Combination in Electricity Price Forecasting: An Application of the ARHNN Method
Tomasz Serafin, Weronika Nitka
Calibration sample selection and forecast combination are two simple yet powerful tools used in forecasting. They can be combined with a variety of models to significantly improve…
stat.AP2025
Probabilistic intraday electricity price forecasting using generative machine learning
Jieyu Chen, Sebastian Lerch, Melanie Schienle +2
The growing importance of intraday electricity trading in Europe calls for improved price forecasting and tailored decision-support tools. In this paper, we propose a novel generat…
stat.AP2024
Probabilistic forecasting with a hybrid Factor-QRA approach: Application to electricity trading
Katarzyna Maciejowska, Tomasz Serafin, Bartosz Uniejewski
This paper presents a novel hybrid approach for constricting probabilistic forecasts that combines both the Quantile Regression Averaging (QRA) method and the factor-based averagin…