Recent advancements in foundation models have been successfully extended to the time series (TS) domain, facilitated by the emergence of large-scale TS datasets. However, previous efforts have primarily focused on designing model architectures to address explicit heterogeneity among datasets such as various numbers of channels, while often overlooking implicit heterogeneity such as varying dependencies between channels. In this work, we introduce the concept of partial channel dependence (PCD), which enables a more sophisticated adjustment of channel dependencies based on dataset-specific information. To achieve PCD, we propose a channel mask that captures the relationships between channels within a dataset using two key components: 1) a correlation matrix that encodes relative dependencies between channels, and 2) domain parameters that learn the absolute dependencies specific to each dataset, refining the correlation matrix. We validate the effectiveness of PCD across four tasks in TS including forecasting, classification, imputation, and anomaly detection, under diverse settings, including few-shot and zero-shot scenarios with both TS foundation models and single-task models. Code is available at https://github.com/seunghan96/CM.

翻译：近期，基础模型的进展已成功扩展至时间序列领域，这得益于大规模时间序列数据集的涌现。然而，先前的研究主要集中于设计模型架构以处理数据集间的显式异质性（如通道数量的差异），而往往忽略了隐式异质性（如通道间依赖关系的多样性）。本文中，我们提出了部分通道依赖的概念，该概念能够基于数据集特定信息更精细地调整通道间的依赖关系。为实现部分通道依赖，我们提出了一种通道掩码，该掩码通过两个关键组件捕捉数据集中通道间的关系：1）编码通道间相对依赖关系的相关矩阵；2）学习每个数据集特有绝对依赖关系的领域参数，用于优化相关矩阵。我们在时间序列的四个任务（包括预测、分类、填补和异常检测）中验证了部分通道依赖的有效性，实验涵盖了多种设置，包括少样本和零样本场景，并同时应用于时间序列基础模型和单任务模型。代码可在 https://github.com/seunghan96/CM 获取。