GITCO: Gated Inference-Time Context Optimization in TSFMs
Quick Answer
This paper shows that GITCO, a novel framework for Time Series Foundation Models, optimizes input context to mitigate context poisoning, achieving a +1.95% MASE reduction on TimesFM 2.5 across 53 datasets without modifying model parameters.
Quick Take
This method captures 89.9% of the potential accuracy improvement by suppressing harmful patches.
Key Points
- GITCO consists of three components: Gate, Router, and Critic.
- Achieves +1.95% MASE reduction on TimesFM 2.5 during K-fold cross-validation.
- Identifies and suppresses harmful patches without parameter updates.
- Introduces context sensitivity profiles for TSFMs based on meta-features.
- Evaluated across 53 GIFT-Eval datasets, demonstrating significant performance gains.
Paper Resources
Article Excerpt
From source RSS / original summaryarXiv:2606. 05332v1 Announce Type: new Abstract: Patch-based Time Series Foundation Models (TSFMs) suffer from context poisoning: structurally anomalous patches capture disproportionate attention and silently degrade zero-shot forecast quality. We propose improving TSFM accuracy at inference time by optimizing the input context rather than modifying model weights.
We present GITCO (Gated Inference-Time Context Optimization), a lightweight three-component framework: Gate, Router, and Critic that selectively identifies and suppresses harmful patches without any parameter updates. Evaluated on TimesFM 2. 5 across 53 GIFT-Eval datasets under K-fold cross-validation, GITCO achieves an average +1. 95% MASE reduction on TimesFM 2. 5 while capturing 89. 9% of the improvement upper bound.
We introduce context sensitivity profiles as a new characterizable property of TSFMs: the mapping from time series meta-features to expected accuracy improvement under inference-time context intervention, shaped jointly by model architecture and the statistical structure of the data.
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