Exploring the Interpretability of Forecasting Models for Energy Balancing Market

Battle Count: 77

The paper is primarily focused on energy market forecasting (balancing market prices) rather than traditional financial asset trading. However, the methodology of comparing interpretable vs black-box models, the use of gradient boosting ensembles, and the accuracy-interpretability trade-off analysis are directly transferable to quantitative trading contexts. The energy market forecasting problem shares characteristics with financial time series prediction (volatility, regime changes, non-linear drivers). The interpretability focus is relevant for risk management in trading systems.

The models used (XGBoost, EBM) are well-established and available in standard libraries (InterpretML for EBM, xgboost for XGBoost). The stacked ensemble architecture is straightforward. The main complexity lies in feature engineering for the energy domain, handling the expanding window cross-validation, and interpreting the EBM shape functions. Hyperparameter tuning via grid search adds computational cost but is standard practice.

The paper describes the experimental setup including data source (V olue Insight API), temporal resolution (15-minute), feature set, expanding window cross-validation, and grid search for hyperparameters. However, no GitHub repository or code is provided. The data is from a commercial API, limiting full reproducibility. The models (XGBoost, EBM) are standard open-source implementations.