ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning

• URL: http://arxiv.org/abs/2608.24033v1
• Date: Tue, 25 Aug 2026 03:45:24 GMT
• Title: ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning
• Abstract summary: We introduce ChorusTIC, a classification-native foundation model for in-context classification across heterogeneous channel configurations.<n>ChorusTIC maps variable channel configurations into a fixed-width representation independent of the original channel count.<n>It calibrates feature axes using context-derived distributions and predicts query labels through leakage-protected in-context learning.
• Score: 52.72143039958434
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time series classification underpins applications in healthcare, sensing, and industrial monitoring. Although time series foundation models support forecasting and transferable representation learning, classification still typically requires fitting a task-specific classifier on each target dataset, while individual channels of multivariate inputs are often encoded independently. We introduce ChorusTIC, a classification-native foundation model for in-context classification across heterogeneous channel configurations without target-task parameter updates. ChorusTIC combines episode-consistent Random Subchannel Slot Concatenation with a shared dual-axis encoder to model temporal and cross-channel interactions and map variable channel configurations into a fixed-width representation independent of the original channel count. It then calibrates feature axes using context-derived distributions and predicts query labels through leakage-protected in-context learning. We pretrain ChorusTIC solely on synthetic labeled episodes comprising context and query sets that share a task background, with classes distinguished by sparse temporal or cross-channel rules. Evaluations on the complete UEA-30 and UCR-128 archives show strong full-context and low-label performance without target-specific classifier fitting.

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