Partial Channel Dependence with Channel Masks for Time Series Foundation Models
Keywords: Time Series, Foundation Model, Channel Dependence, Transformer
TL;DR: We introduce the concept of partial channel dependence (PCD) to partially adjust the channel dependence (CD) captured by the model through the proposed channel mask (CM), which contains dataset-specific information.
Abstract: 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) for models capturing channel dependencies (CDs) via attention, which enables a more sophisticated adjustment of CDs 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.
Supplementary Material: zip
Primary Area: learning on time series and dynamical systems
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Submission Number: 54
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