Univariate Channel Fusion for Multivariate Time Series Classification
For practitioners needing real-time or low-resource multivariate time series classification, UCF offers a computationally lightweight alternative to complex deep learning models.
The paper proposes Univariate Channel Fusion (UCF), a method that transforms multivariate time series into a univariate representation using simple fusion strategies, enabling efficient classification with univariate classifiers. UCF outperforms state-of-the-art methods in five case studies while achieving substantial computational efficiency gains.
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.