3 papers in the archive (2025), average empirical rigor 8.3/10, average math complexity 6/10. Quadrant mix: Holy Grail ×3.
Works mostly in Commodities & Energy Markets, Machine Learning for Market Forecasting.
Frequent co-authors: Bartosz Uniejewski (2), Katarzyna Maciejowska (1), Kaja Bilinska (1), Nicolaos Kourentzes (1), Rafal Weron (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
Quantifying the uncertainty of forecasting models is essential to assess and mitigate the risks associated with data-driven decisions, especially in volatile domains such as electricity markets. Machine learning methods can provide highly accurate electricity price forecasts, critical for informing
We introduce the concept of temporal hierarchy forecasting (THieF) in predicting day-ahead electricity prices and show that reconciling forecasts for hourly products, 2- to 12-hour blocks, and baseload contracts significantly (up to 13%) improves accuracy at all levels. These results remain consiste
In recent years, a rapid development of forecasting methods has led to an increase in the accuracy of predictions. In the literature, forecasts are typically evaluated using metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). While appropriate for statistical assessment, th