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

TL;DR

This paper presents RED CoMETS, an ensemble classifier for multivariate time series that improves accuracy and efficiency by extending a univariate symbolically represented classifier to handle multivariate data, with strong results on benchmark datasets.

Contribution

The paper introduces RED CoMETS, a novel ensemble classifier that extends Co-eye to multivariate data, achieving state-of-the-art accuracy and reduced computation time.

Findings

Achieves highest accuracy on 'HandMovementDirection' dataset.

Demonstrates competitive accuracy on UCR benchmark datasets.

Reduces computation time compared to previous methods.

Abstract

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.