对比三款时序大模型，发现Chronos-2在比利时电价预测中表现最佳。
Empirical evaluation of Time Series Foundation Models for Day-ahead and Imbalance Electricity Price Forecasting in Belgium
- 用Chronos-2的ARX模式进行零样本预测，无需任务微调。
- 日间市场预测MAE比最优机器学习方法低5%，但不平衡市场误差高10%。
- 模型在极端行情下仍不稳健，适合对精度要求高的电力市场研究者。
近期时序基础模型（TSFMs）展现出无需任务微调即可实现零样本预测的潜力。尽管这些模型在通用基准上表现优异，但在波动性强、复杂的电力市场中的适用性仍待探索。本研究系统评估了Amazon开发的Chronos-2和Chronos-Bolt，以及Google提供的TimesFM 2.5，在比利时日间及不平衡电力价格预测中的表现。结果显示，Chronos-2在ARX模式下对两类市场均取得最优结果。相比其他机器学习方法的最佳集成模型，其日间市场价格预测平均绝对误差（MAE）降低5%；而在所有预测时距下，不平衡价格预测的MAE高出10%，仅两小时前瞻除外。此外，研究发现TSFMs具备真正的零样本预测能力，但在极端市场条件下仍表现不足。
原文摘要 · Abstract (English)
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.
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