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

2026-08-24

AI Summary

ChorusTIC通过无训练的上下文学习方法解决多变量时间序列分类问题，无需为目标任务调整参数。

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.

Problem

• Time Series Classification
• Multivariate Inputs
• In-Context Learning

Innovation

• Training-Free
• In-Context Learning
• Multivariate Time Series Classification
• Random Subchannel Slot Concatenation
• Dual-Axis Encoder