提出通道归一化方法，让时间序列模型区分不同通道特征。
Channel Normalization for Time Series Channel Identification
- 为每通道分配独立仿射参数，增强通道可识别性
- 在多种模型上提升性能，尤其对非可识别模型效果显著
- 支持动态调整与未知通道数场景，适合基础模型应用
通道可识别性（CID）指时间序列建模中区分各通道的能力。缺乏CID会导致相同输入产生相同输出，忽略通道特异性。本文强调CID重要性，提出通道归一化（CN），通过为每个通道分配独立仿射参数，有效提升通道可识别性。进一步扩展出两种变体：自适应通道归一化（ACN）根据输入动态调整参数，增强模型适应性；原型通道归一化（PCN）引入可学习原型替代通道级参数，适用于通道数量未知或变化的数据集，便于在时间序列基础模型中使用。我们在多种时间序列模型上验证了CN及其变体的有效性，显著提升了非可识别与可识别模型的性能。同时从信息论角度分析了方法成功的原因。代码已开源：https://github.com/seunghan96/CN。
原文摘要 · Abstract (English)
Channel identifiability (CID) refers to the ability to distinguish between individual channels in time series (TS) modeling. The absence of CID often results in producing identical outputs for identical inputs, disregarding channel-specific characteristics. In this paper, we highlight the importance of CID and propose Channel Normalization (CN), a simple yet effective normalization strategy that enhances CID by assigning distinct affine transformation parameters to each channel. We further extend CN in two ways: 1) Adaptive CN (ACN) dynamically adjusts parameters based on the input TS, improving adaptability in TS models, and 2) Prototypical CN (PCN) introduces a set of learnable prototypes instead of per-channel parameters, enabling applicability to datasets with unknown or varying number of channels and facilitating use in TS foundation models. We demonstrate the effectiveness of CN and its variants by applying them to various TS models, achieving significant performance gains for both non-CID and CID models. In addition, we analyze the success of our approach from an information theory perspective. Code is available at https://github.com/seunghan96/CN.
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