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This research addresses the critical challenge of adapting Time Series Foundation Models in black-box scenarios where direct parameter access is unavailable. By leveraging predictive errors conditioned on input-output contexts, the proposed ORCA method enables effective online adaptation without relying on traditional gradient backpropagation or white-box fine-tuning. The study validates this approach across multiple state-of-the-art models and datasets, offering a practical pathway for deploying closed-source forecasting APIs in dynamic environments.
Learning the Context of Errors: Black-Box Online Adaptation of Time Series Foundation Models
arXiv:2606.14222v1 Announce Type: new Abstract: 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.
Abstract: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 this https URL.
Machine Learning (cs.LG)
arXiv:2606.14222 [cs.LG]
(or arXiv:2606.14222v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.14222 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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