用简单融合方法把多变量时间序列变单变量，提升分类效率。
Univariate Channel Fusion for Multivariate Time Series Classification
- 将多变量时间序列通过均值、中位数等融合为单变量输入
- 在多个领域实验中表现优于主流方法，计算量大幅降低
- 适合高通道相关性场景，尤其适用于低算力设备部署
多变量时间序列分类（MTSC）在生物医学信号分析和运动监测等领域至关重要。然而，现有深度学习方法通常需要大量计算资源，难以用于实时应用或低成本硬件（如物联网设备和可穿戴系统）。本文提出一种无监督通道融合（Univariate Channel Fusion, UCF）方法，通过均值、中位数或动态时间规整巴氏中心等简单策略，将多变量时间序列转化为单变量表示，从而兼容任何专为单变量设计的分类器。该方法提供了一种灵活且计算轻量的替代方案。我们在五个案例研究中评估了UCF，涵盖化学监测、脑机接口和人体活动分析等多个领域。结果表明，UCF在多数情况下优于基线方法和针对MTSC设计的先进算法，同时显著提升计算效率，尤其在通道间相关性较高的任务中表现优异。
原文摘要 · Abstract (English)
Multivariate time series classification (MTSC) plays a crucial role in various domains, including biomedical signal analysis and motion monitoring. However, existing approaches, particularly deep learning models, often require high computational resources, making them unsuitable for real-time applications or deployment on low-cost hardware, such as IoT devices and wearable systems. In this paper, we propose the Univariate Channel Fusion (UCF) method to deal with MTSC efficiently. UCF transforms multivariate time series into a univariate representation through simple channel fusion strategies such as the mean, median, or dynamic time warping barycenter. This transformation enables the use of any classifier originally designed for univariate time series, providing a flexible and computationally lightweight alternative to complex models. We evaluate UCF in five case studies covering diverse application domains, including chemical monitoring, brain-computer interfaces, and human activity analysis. The results demonstrate that UCF often outperforms baseline methods and state-of-the-art algorithms tailored for MTSC, while achieving substantial gains in computational efficiency, being particularly effective in problems with high inter-channel correlation.
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