Learning the Context of Errors: Black-Box Online Adaptation of Time Series Foundation Models

2026-06-12

AI Summary

This work addresses the challenge of online adaptation for black-box time series foundation models when access to internal parameters is unavailable. The authors propose ORCA, a novel approach that explicitly learns the mapping between prediction errors and input-output contextual information, enabling dynamic correction of forecasts through residual modeling—without modifying the original model or relying on gradient-based updates. By circumventing the conventional paradigm of white-box fine-tuning, ORCA demonstrates superior performance over existing black-box adaptation strategies across five state-of-the-art foundation models and eight benchmark datasets, establishing its effectiveness and broad applicability.

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.

Problem

• Time Series Foundation Models
• Black-box Adaptation
• Online Adaptation
• Predictive Errors
• Context of Errors

Innovation

• black-box adaptation
• time series foundation models
• online adaptation
• error context
• ORCA