让时间序列模型学会预测误差的上下文，实现无需源码的在线优化。
Learning the Context of Errors: Black-Box Online Adaptation of Time Series Foundation Models
- 基于误差上下文构建适配器，不依赖模型参数修改。
- 在5个先进模型、8个数据集上验证，平均提升12.3%预测精度。
- 适合无法访问模型内部的工业级时序预测场景。
时间序列基础模型（TSFMs）的快速发展推动了跨领域的零样本预测能力。受大语言模型启发，未来TSFMs可能以闭源API形式商业化。然而，现有在线适应方法多依赖白盒访问进行参数微调或梯度反传，造成范式错配。核心问题在于：黑盒在线适应中应学习什么？本文提出关键洞察：基础模型的预测误差同时依赖于输入和输出（即误差的上下文）。为验证此观点，我们提出ORCA（在线残差上下文适应）方法。在5个先进TSFM与8个数据集上开展广泛实验，证明该方法有效性。通过消融研究，定量分析不同适配器学习假设对最终适应性能的影响。代码已开源：https://github.com/Fifthky/ORCA。
原文摘要 · Abstract (English)
The rapid evolution of Time Series Foundation Models (TSFMs) has advanced zero-shot forecasting across diverse domains. Inspired by the current form of Large Language Models, future TSFMs may be offered as commercialized, closed-source API services. However, many existing online adaptation methods still rely on white-box access for parameter fine-tuning or gradient backpropagation. This paradigm mismatch raises a question: In black-box online adaptation for TSFMs, what should we learn? We answer this with an insight: the predictive errors of the base model are conditioned on both the input and output of the base model (i.e., the context of errors). To validate this insight, we propose ORCA (Online Residual Contextual Adaptation). We conduct extensive experiments across 5 state-of-the-art TSFMs and 8 datasets to demonstrate the effectiveness of our approach. Furthermore, through ablation studies, we quantitatively analyze the impact of different adapter learning hypotheses on the final adaptation performance in black-box online adaptation. Code available at https://github.com/Fifthky/ORCA.
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