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20012025
most citedNeural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx

259 citations · 1.3k across the 23 of their papers we have counts for

collaborators

24 papers

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…

q-fin.ST2025★ 8 cited

Extrapolating the long-term seasonal component of electricity prices for forecasting in the day-ahead market

Katarzyna Chęć, Bartosz Uniejewski, Rafał Weron

Recent studies provide evidence that decomposing the electricity price into the long-term seasonal component (LTSC) and the remaining part, predicting both separately, and then com…

q-fin.ST2024★ 18 cited

Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression

Arkadiusz Lipiecki, Bartosz Uniejewski, Rafał Weron

Operational decisions relying on predictive distributions of electricity prices can result in significantly higher profits compared to those based solely on point forecasts. Howeve…

q-fin.ST2023★ 1 cited

Combining predictive distributions of electricity prices: Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?

Weronika Nitka, Rafał Weron

Probabilistic price forecasting has recently gained attention in power trading because decisions based on such predictions can yield significantly higher profits than those made wi…

math.OC2023★ 120 cited

Operational Research: Methods and Applications

Fotios Petropoulos, Gilbert Laporte, Emel Aktas +79

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This…

q-fin.ST2022★ 101 cited

Distributional neural networks for electricity price forecasting

Grzegorz Marcjasz, Michał Narajewski, Rafał Weron +1

We present a novel approach to probabilistic electricity price forecasting which utilizes distributional neural networks. The model structure is based on a deep neural network that…