Paper Title
Classification of Time Series through Ensembles of Different Distance Measures

Abstract
This paper presents an ensemble approach for time series classification which is based on elastic and non-elastic distance measures for a 1-NN (one nearest neighbor) method. We also design a multi-threaded implementation for the proposed ensemble method in order to improve its efficiency. Experimental results over a large collection of benchmark datasets showed that our proposed method remarkably outperforms the 1NN with Dynamic Time Warping measure which has been considered in literature as “difficult to beat”. Besides, we compare the efficiency of multi-threaded implementation to that of sequential implementation for the proposed method. Results in the latter experiment showed that in average the multi-threaded version can run faster that the sequential version about 2.8 times. Keywords - Time Series, Classification, Ensemble, Elastic Distance Measure, Non-elastic Distance Measure.