Abstract
Time-series foundation models have emerged as a new paradigm for forecasting, yet their ability to effectively leverage exogenous features -- critical for electricity demand forecasting -- remains unclear. This paper empirically evaluates foundation models capable of modeling cross-channel correlations against a baseline LSTM with reversible instance normalization across Singaporean and Australian electricity markets at hourly and daily granularities. We systematically assess MOIRAI, MOMENT, TinyTimeMixers, ChronosX, and Chronos-2 under three feature configurations: all features, selected features, and target-only. Our findings reveal highly variable effectiveness: while Chronos-2 achieves the best performance among foundation models (in zero-shot settings), the simple baseline frequently outperforms all foundation models in Singapore's stable climate, particularly for short-term horizons. Model architecture proves critical, with synergistic architectural implementations (TTM's channel-mixing, Chronos-2's grouped attention) consistently leveraging exogenous features, while other approaches show inconsistent benefits. Geographic context emerges as equally important, with foundation models demonstrating advantages primarily in variable climates. These results challenge assumptions about universal foundation model superiority and highlight the need for domain-specific models, specifically in the energy domain.
Abstract (translated)
时间序列基础模型作为预测的新范式已经出现，但它们在有效利用外生特征（这对电力需求预测至关重要）方面的能力仍然不明朗。本文通过实证研究，在新加坡和澳大利亚的电力市场中，对能够建模跨通道相关性的基础模型与具有可逆实例归一化的基线LSTM模型进行了比较评估，涵盖了小时级和日级别粒度的数据。我们系统地评估了MOIRAI、MOMENT、TinyTimeMixers（TTM）、ChronosX和Chronos-2这五种基础模型，在三种特征配置下：所有特征、选定特征以及仅目标变量。 研究发现这些模型的有效性差异显著：在零样本设置中，Chronos-2在所有基础模型中的表现最佳；然而，在新加坡稳定气候条件下，简单的基线模型通常能够超越所有其他基础模型，尤其是在短期预测时。此外，模型架构至关重要，具有协同作用的架构实现（如TTM的通道混合、Chronos-2的分组注意力）一致地利用外生特征，而其他方法则表现出不稳定的收益。 地理背景的重要性也得到了证实：在气候变化显著的情况下，基础模型显示出更大的优势。这些结果挑战了关于基础模型普遍优越性的假设，并强调了领域特定模型（特别是在能源领域中）的需求。
URL
https://arxiv.org/abs/2602.05390
