Multivariate time series classification is a rapidly growing research field with practical applications in finance, healthcare, engineering, and more. The complexity of classifying multivariate time series data arises from its high dimensionality, temporal dependencies, and varying lengths. This paper introduces a novel ensemble classifier called RED CoMETS (Random Enhanced Co-eye for Multivariate Time Series), which addresses these challenges. RED CoMETS builds upon the success of Co-eye, an ensemble classifier specifically designed for symbolically represented univariate time series, and extends its capabilities to handle multivariate data. The performance of RED CoMETS is evaluated on benchmark datasets from the UCR archive, where it demonstrates competitive accuracy when compared to state-of-the-art techniques in multivariate settings. Notably, it achieves the highest reported accuracy in the literature for the 'HandMovementDirection' dataset. Moreover, the proposed method significantly reduces computation time compared to Co-eye, making it an efficient and effective choice for multivariate time series classification.

翻译：多变量时间序列分类是一个快速发展的研究领域，在金融、医疗、工程等领域具有实际应用价值。由于数据的高维度、时间依赖性和长度变化性，多变量时间序列分类任务十分复杂。本文提出了一种新型集成分类器RED CoMETS（随机增强多变量时间序列共眼分类器），以应对上述挑战。RED CoMETS基于专为符号表示的单变量时间序列设计的集成分类器Co-eye的成功经验，将其能力扩展至多变量数据。在UCR基准数据集上的评估表明，RED CoMETS在多变量场景下与先进技术相比具有竞争性的分类精度。值得注意的是，它在'HandMovementDirection'数据集上取得了文献中的最高报告精度。此外，与Co-eye相比，该方法显著减少了计算时间，成为多变量时间序列分类的高效且有效的选择。