Channel Normalization for Time Series Channel Identification

2025-05-31

AI Summary

A pervasive channel indistinguishability (CID) problem in multivariate time series modeling leads to homogeneous outputs and hinders the capture of channel-specific patterns. To address this, we propose Channel Normalization (CN), a general framework that introduces independent, learnable, channel-wise affine transformations. We further design two variants: Adaptive CN—which dynamically modulates parameters conditioned on input features—and Prototype CN—which supports unknown or variable numbers of channels. This work is the first to explicitly incorporate CID modeling into time-series foundation models. Through information-theoretic analysis, our approach consistently improves performance across diverse state-of-the-art time-series architectures, delivering unified gains in both CID and non-CID scenarios. Extensive experiments validate its effectiveness and generalizability. The implementation is publicly available.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Computer Vision: Multi-modal Vision
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Web Mining and Content Analysis: Models for Web evolution

Abstract

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.

Problem

• Enhancing channel identifiability in time series modeling
• Preventing identical outputs for identical inputs across channels
• Adapting normalization for varying channel numbers in datasets

Innovation

• Channel Normalization enhances channel identifiability
• Adaptive CN dynamically adjusts transformation parameters
• Prototypical CN uses learnable prototypes for flexibility