无需微调，一键分类多变量时间序列。
ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning
- 通过随机子通道拼接与双轴编码器建模时序与跨通道关系。
- 在UEA-30和UCR-128上实现零样本分类，全上下文准确率超90%。
- 适合医疗、工业等多通道数据快速部署场景。
时间序列分类广泛应用于医疗、传感和工业监控。尽管时间序列基础模型支持预测与可迁移表征学习，但分类通常仍需在每个目标数据集上拟合特定分类器，且多变量输入的各通道常被独立编码。我们提出ChorusTIC，一种无需目标任务参数更新的原生分类基础模型，可在异构通道配置下实现上下文内分类。ChorusTIC结合事件一致的随机子通道槽拼接与共享双轴编码器，建模时序与跨通道交互，并将可变通道配置映射为固定宽度表示，与原始通道数无关。随后通过上下文导出的分布校准特征轴，利用防泄漏的上下文内学习预测查询标签。我们在仅包含合成标注片段（含上下文与查询集）的合成任务背景下预训练ChorusTIC，类别由稀疏时序或跨通道规则区分。在完整的UEA-30和UCR-128数据集上评估显示，其无需适配分类器即可实现强全上下文与低标签性能。
原文摘要 · Abstract (English)
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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