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
The proliferation of advanced Time Series Foundation Models (TSFMs) and the trend towards closed-source, API-driven AI services necessitate new black-box adaptation methods to maintain utility and performance.
This research addresses a critical challenge in deploying future TSFMs by enabling continuous adaptation without direct access to model internals, crucial for commercial viability and broader application.
The ability to perform online adaptation for TSFMs in a black-box setting shifts the paradigm from requiring white-box access to focusing on observable predictive errors for model improvement.
- · AI platform providers
- · Time series data analytics
- · Industries relying on forecasting
- · Companies with solely white-box adaptation strategies
Black-box online adaptation will allow TSFMs to be integrated more widely into legacy systems and commercial applications without requiring deep technical knowledge of the underlying model.
This could accelerate the adoption of TSFMs as reliable, continuously improving services, driving further innovation in data-driven decision-making across various sectors.
The development of robust black-box adaptation techniques might reduce the competitive advantage of fully transparent, open-source model providers, shifting the focus to performance and service delivery.
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Read at arXiv cs.LG