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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 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 complexity 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 FastShapelet 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. [ABSTRACT FROM AUTHOR]

Abstract: In time series predictor evaluation, we observe that with respect to the model selection procedure there is a gap between evaluation of traditional forecasting procedures, on the one hand, and evaluation of machine learning techniques on the other hand. In traditional forecasting, it is common practice to reserve a part from the end of each time series for testing, and to use the rest of the series for training. Thus it is not made full use of the data, but theoretical problems with respect to temporal evolutionary effects and dependencies within the data as well as practical problems regarding missing values are eliminated. On the other hand, when evaluating machine learning and other regression methods used for time series forecasting, often cross-validation is used for evaluation, paying little attention to the fact that those theoretical problems invalidate the fundamental assumptions of cross-validation. To close this gap and examine the consequences of different model selection procedures in practice, we have developed a rigorous and extensive empirical study. Six different model selection procedures, based on (i) cross-validation and (ii) evaluation using the series’ last part, are used to assess the performance of four machine learning and other regression techniques on synthetic and real-world time series. No practical consequences of the theoretical flaws were found during our study, but the use of cross-validation techniques led to a more robust model selection. To make use of the “best of both worlds”, we suggest that the use of a blocked form of cross-validation for time series evaluation became the standard procedure, thus using all available information and circumventing the theoretical problems. [Copyright &y& Elsevier]