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Multivariate time series classification is a rapidly growing research fieldwith practical applications in finance, healthcare, engineering, and more. Thecomplexity of classifying multivariate time series data arises from its highdimensionality, temporal dependencies, and varying lengths. This paperintroduces a novel ensemble classifier called RED CoMETS (Random EnhancedCo-eye for Multivariate Time Series), which addresses these challenges. REDCoMETS builds upon the success of Co-eye, an ensemble classifier specificallydesigned for symbolically represented univariate time series, and extends itscapabilities to handle multivariate data. The performance of RED CoMETS isevaluated on benchmark datasets from the UCR archive, where it demonstratescompetitive accuracy when compared to state-of-the-art techniques inmultivariate settings. Notably, it achieves the highest reported accuracy inthe literature for the 'HandMovementDirection' dataset. Moreover, the proposedmethod significantly reduces computation time compared to Co-eye, making it anefficient and effective choice for multivariate time series classification.

RED CoMETS: An ensemble classifier for symbolically represented multivariate time series