  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.
