ChorusTIC：基于上下文学习的无训练多变量时间序列分类方法

中文摘要 AI 辅助

时间序列分类是医疗、传感和工业监控等应用的基础。尽管时间序列基础模型支持预测和可迁移表示学习，但分类通常仍需要在每个目标数据集上拟合任务特定的分类器，而多变量输入的各个通道往往被独立编码。本文提出ChorusTIC，一种原生用于分类的基础模型，可针对异构通道配置执行上下文分类，无需更新目标任务参数。ChorusTIC结合了与episode一致的随机子通道槽拼接方法和共享双轴编码器，以建模时间和跨通道交互，并将可变通道配置映射为与原始通道数无关的固定宽度表示；随后利用上下文衍生分布校准特征轴，通过防泄漏的上下文学习预测查询标签。我们仅在包含上下文集和查询集的合成标记episode上预训练ChorusTIC，其中两类通过稀疏时间或跨通道规则区分。在完整的UEA-30和UCR-128数据集上的评估显示，该方法在无需拟合目标特定分类器的情况下，具备优异的全上下文性能和低标签性能。

英文摘要

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.

发表机构

• Guangdong University of Technology（广东工业大学）
• Université Paris Cité（巴黎西岱大学）
• Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
• Shantou University（汕头大学）

机构由 AI 辅助整理，请以论文原文为准。