GITCO: Gated Inference-Time Context Optimization in TSFMs

2026-06-03

AI Summary

This work addresses the vulnerability of patch-based time series foundation models (TSFMs) to contextual contamination from structurally anomalous patches during inference, which degrades zero-shot forecasting performance. To mitigate this issue without updating model parameters, the authors propose GITCO—a lightweight inference-time framework comprising three components: a Gate, a Router, and a Critic. GITCO introduces, for the first time, a context sensitivity profile to dynamically identify and suppress harmful patches by selectively modulating their attention weights. Evaluated on TimesFM 2.5 across 53 datasets in the GIFT-Eval benchmark, GITCO reduces average MASE by 1.95%, achieving 89.9% of the theoretical improvement ceiling and substantially enhancing zero-shot prediction accuracy.

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.

Problem

• context poisoning
• Time Series Foundation Models
• zero-shot forecasting
• anomalous patches
• inference-time context

Innovation

• Inference-Time Optimization
• Context Poisoning
• Time Series Foundation Models
• Patch-Based Forecasting
• Zero-Shot Forecasting