COMPARING EFFICIENCY BETWEEN TWO MEASURES OF EUCLID AND DTW USED IN DISCOVERY MOTIF IN TIME SERIES

Abstract

The study on time series databases which is based on efficient retrieval of unknown patterns and frequently encountered in time series, called motif, has attracted much attention from many researchers recently. These motifs are very useful for the exploration of data and provide solutions to many problems in various fields of applications. In this paper, we try to study and evaluate the efficiency of the use of both Euclidean and Dynamic Time Warping (DTW) distance meassures, utilizing Bruteforce and Mueen – Keogh algorithms (MK), of which MK algorithm has performed efficiently in terms of CPU time and the accuracy of the problem of discovery the motif patterns. The efficiency of this method has been proven through experiment on real databases.