Baguan-TS: A Sequence-Native In-Context Learning Model for Time Series Forecasting with Covariates

• URL: http://arxiv.org/abs/2603.17439v1
• Date: Wed, 18 Mar 2026 07:24:19 GMT
• Title: Baguan-TS: A Sequence-Native In-Context Learning Model for Time Series Forecasting with Covariates
• Abstract summary: Baguan-TS integrates the raw-sequence representation learning with ICL, instantiated by a 3D Transformer.<n>We tackle two key hurdles: (i) calibration and training stability, improved with a feature-agnostic, target-space retrieval-based local calibration, and (ii) output oversmoothing, mitigated via context-overfitting strategy.
• Score: 31.296823831987748
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformers enable in-context learning (ICL) for rapid, gradient-free adaptation in time series forecasting, yet most ICL-style approaches rely on tabularized, hand-crafted features, while end-to-end sequence models lack inference-time adaptation. We bridge this gap with a unified framework, Baguan-TS, which integrates the raw-sequence representation learning with ICL, instantiated by a 3D Transformer that attends jointly over temporal, variable, and context axes. To make this high-capacity model practical, we tackle two key hurdles: (i) calibration and training stability, improved with a feature-agnostic, target-space retrieval-based local calibration; and (ii) output oversmoothing, mitigated via context-overfitting strategy. On public benchmark with covariates, Baguan-TS consistently outperforms established baselines, achieving the highest win rate and significant reductions in both point and probabilistic forecasting metrics. Further evaluations across diverse real-world energy datasets demonstrate its robustness, yielding substantial improvements.

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