Empirical evaluation of Time Series Foundation Models for Day-ahead and Imbalance Electricity Price Forecasting in Belgium
For energy market participants, this paper provides an empirical assessment of TSFMs in a volatile domain, revealing mixed performance and limitations in extreme scenarios.
This study evaluates Time Series Foundation Models (Chronos-2, Chronos-Bolt, TimesFM) for forecasting Belgian day-ahead and imbalance electricity prices. Chronos-2 in ARX mode achieves 5% lower MAE than the best ensemble for day-ahead prices but 10% higher MAE for imbalance prices, showing zero-shot skills but struggles under extreme conditions.
Recent advances in Time Series Foundation Models (TSFMs) promise zero-shot forecasting capabilities with minimal task-specific training. While these models have shown strong performance across generic benchmarks, their applicability in volatile, complex electricity markets remains underexplored. Addressing this gap, this study provides a systematic empirical evaluation of several TSFMs, specifically Chronos-2 and Chronos-Bolt (developed by Amazon), and TimesFM 2.5 (provided by Google), for forecasting Belgian day-ahead and imbalance electricity prices. For both considered markets, Chronos-2 in ARX mode produces the most accurate forecasts. Compared with the best ensemble prediction from other machine learning methods, Chronos-2's Mean Absolute Error (MAE) is 5% lower for the day-ahead market. In contrast, the model yields 10% higher MAE predicting imbalance prices across all forecast horizons, except for the two-hour-ahead horizon. Moreover, we find that TSFMs exhibit genuine zero-shot forecasting skills but still struggle under extreme market conditions.