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Abstract:
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Time series data are ubiquitous nowadays. Whereas most of the literature on the topic deals with univariate time series, multivariate time series have typically received much less attention. However, the development of machine learning algorithms for the latter objects has substantially increased in recent years due to the advancement of technology and storage capabilities of everyday devices. The R package mlmts attempts to provide a set of peer-reviewed, widespread data mining techniques for multivariate series. Several functions allowing the execution of clustering, classification or outlier detection methods, among others, are included in the package. mlmts also incorporates a collection of multivariate time series datasets which are often used to analyse the performance of new classification algorithms. The main characteristics of the package are described and its use is illustrated through various case studies. Practitioners from a wide variety of fields could benefit from the general framework provided by mlmts.
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