4 papers in the archive (2025–2026), average empirical rigor 7.4/10, average math complexity 4.5/10. Quadrant mix: Holy Grail ×2, Street Traders ×2.
Works mostly in Commodities & Energy Markets, Machine Learning for Market Forecasting, High-Frequency Trading & Optimal Execution.
Frequent co-authors: Jochen L. Cremer (4), Derek W. Bunn (3), Julia Lin (2), Jochen Stiasny (2), Wentao Wang (2), Yujie Chen (2).
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%).
Accurate probabilistic forecasting of intraday electricity prices is critical for market participants to inform trading decisions. Existing studies rely on specific domain features, such as Volume-Weighted Average Price (VWAP) and the last price. However, the rich information in the orderbook remain
Accurate and efficient imbalance electricity price forecasting is critical for industrial energy trading systems, especially as battery assets and automated bidding pipelines increasingly participate in balancing markets. However, real-time forecasting is complicated by nonlinear market-rule-based p
Power systems with increasing variable renewable generation face greater uncertainty in scheduling and balancing. Intraday and balancing electricity markets facilitate position adjustments and real-time balancing close to delivery. As delivery approaches, continuous intraday market participants expo
Electricity price forecasting (EPF) plays a critical role in power system operation and market decision making. While existing review studies have provided valuable insights into forecasting horizons, market mechanisms, and evaluation practices, the rapid adoption of deep learning has introduced inc