RED CoMETS: An ensemble classifier for symbolically represented multivariate time series
This work addresses the challenge of multivariate time series classification, which has applications in finance, healthcare, and engineering, but it is incremental as it builds upon an existing univariate method.
The paper tackles the problem of classifying multivariate time series by introducing RED CoMETS, an ensemble classifier that extends a univariate method to handle multivariate data, achieving the highest reported accuracy on the 'HandMovementDirection' dataset and reducing computation time compared to its predecessor.
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.