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Shapelets are discriminative time series subsequences that allow generationof interpretable classification models, which provide faster and generallybetter classification than the nearest neighbor approach. However, the shapeletdiscovery process requires the evaluation of all possible subsequences of alltime series in the training set, making it extremely computation intensive.Consequently, shapelet discovery for large time series datasets quickly becomesintractable. A number of improvements have been proposed to reduce the trainingtime. These techniques use approximation or discretization and often lead toreduced classification accuracy compared to the exact method.  We are proposing the use of ensembles of shapelet-based classifiers obtainedusing random sampling of the shapelet candidates. Using random sampling reducesthe number of evaluated candidates and consequently the required computationalcost, while the classification accuracy of the resulting models is also notsignificantly different than that of the exact algorithm. The combination ofrandomized classifiers rectifies the inaccuracies of individual models becauseof the diversity of the solutions. Based on the experiments performed, it isshown that the proposed approach of using an ensemble of inexpensiveclassifiers provides better classification accuracy compared to the exactmethod at a significantly lesser computational cost.

Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy while Reducing Computational Costs