TED MANUSC The classification of time series is a central problem in a wide range of dis ciplines. In this field, the state-of-the-art algorithm is COTE (Collective of Transformation-Based Ensembles) which is a combination of classifiers of dif ferent domains: time, autocorrelation, power spectrum and shapelets. The weakest point of this approach is its high computational burden which prevents its use in massive data environments. Shapelet Transform is one of the multiple algorithms that compose this ensemble. It has been shown to achieve a good performance over many reference datasets. Nevertheless, its computational com plexity is also too high to be used in massive 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 an algorithm for time series classification in a Big Data environment, DFST. It is based on a combination of the Fast Shapelet and Shapelet Transform ideas and it is the first completely scalable algorithm for time series classification. We have shown that our proposal scales linearly with the number of time series in dataset. In addition, the classification accuracy is equal to or higher than that of comparable sequential algorithms.
