🤖 AI Summary
This work addresses the high computational cost of multivariate time series classification (MTSC) models, which hinders their real-time deployment on resource-constrained devices. To overcome this limitation, the authors propose a lightweight channel fusion mechanism that compresses multivariate sequences into univariate representations using strategies such as mean, median, or dynamic time warping (DTW) barycenter aggregation, thereby enabling compatibility with efficient off-the-shelf univariate classifiers. The approach proves particularly effective when channels exhibit strong inter-variable correlations. Evaluated across five diverse datasets spanning chemical monitoring, brain–computer interfaces, and human activity recognition, the method not only significantly outperforms existing MTSC baselines and state-of-the-art approaches but also achieves substantial reductions in computational complexity, effectively balancing high accuracy with high efficiency.
📝 Abstract
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.