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.

翻译：多变量时间序列分类（MTSC）在生物医学信号分析、运动监测等多个领域发挥着关键作用。然而，现有方法（尤其是深度学习模型）通常需要较高的计算资源，使其难以适用于实时应用或部署在低成本硬件（如物联网设备和可穿戴系统）上。本文提出单变量通道融合（UCF）方法以高效处理MTSC问题。UCF通过简单的通道融合策略（如均值、中位数或动态时间规整重心）将多变量时间序列转化为单变量表示。这种转换使得任何原本针对单变量时间序列设计的分类器均可应用，从而为复杂模型提供了灵活且计算轻量化的替代方案。我们通过涵盖化学监测、脑机接口和人体活动分析等不同应用领域的五个案例研究对UCF进行验证。结果表明，UCF在计算效率上获得显著提升的同时，其性能通常优于为MTSC量身定制的基线方法和现有最优算法，尤其在高通道间相关性问题上表现突出。