Rethinking Zero-Shot Time Series Classification: From Task-specific Classifiers to In-Context Inference

• URL: http://arxiv.org/abs/2602.00620v1
• Date: Sat, 31 Jan 2026 09:17:45 GMT
• Title: Rethinking Zero-Shot Time Series Classification: From Task-specific Classifiers to In-Context Inference
• Authors: Juntao Fang, Shifeng Xie, Shengbin Nie, Yuhui Ling, Yuming Liu, Zijian Li, Keli Zhang, Lujia Pan, Themis Palpanas, Ruichu Cai,
• Abstract summary: We propose TIC-FM, an in-context learning framework that treats the labeled training set as context and predicts labels for all test instances in a single forward pass.<n>TIC-FM pairs a time series encoder and a lightweight projection adapter with a split-masked latent memory Transformer.<n>Experiments on 128 UCR datasets show strong accuracy, with consistent gains in the extreme low-label situation.
• Score: 34.15455500037426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The zero-shot evaluation of time series foundation models (TSFMs) for classification typically uses a frozen encoder followed by a task-specific classifier. However, this practice violates the training-free premise of zero-shot deployment and introduces evaluation bias due to classifier-dependent training choices. To address this issue, we propose TIC-FM, an in-context learning framework that treats the labeled training set as context and predicts labels for all test instances in a single forward pass, without parameter updates. TIC-FM pairs a time series encoder and a lightweight projection adapter with a split-masked latent memory Transformer. We further provide theoretical justification that in-context inference can subsume trained classifiers and can emulate gradient-based classifier training within a single forward pass. Experiments on 128 UCR datasets show strong accuracy, with consistent gains in the extreme low-label situation, highlighting training-free transfer

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