Distributed FastShapelet Transform: a Big Data time series classification algorithm
Abstract—The classification of time series is a central problem in a wide range of disciplines. In this field, the state-of-the-art algorithm is COTE (Collective of Transformation-Based Ensembles) which is a combination of classifiers of different domains: time, autocorrelation, power spectrum and shapelets. The weakest point of this approach is its high computational burden which prevents its use in mass data environments. Shapelet Transform is one of the multiple algorithms that compose this ensemble. Besides, it has demonstrated to achieve a good performance over many reference datasets. Nevertheless, its computational complexity is also too high to be used in mass data environments. On the other hand, Big Data has emerged as an approach to manage massive datasets, which also applies to time series. We propose the use of the Shapelet Transform based on the shapelet search algorithm FastShapelet for a Big Data environment. We have shown that our proposal scales linearly with the number of time series in dataset. In addition, the classification accuracy is equal or higher than that of comparable sequential algorithms.
Keywords—Time series, Big Data, Classification, Shapelet