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An improved morphological weighted dynamic similarity measurement algorithm for time series data

International Journal of Intelligent Computing and Cybernetics 2018年第4期11卷 486-495页

作者： Ke Yi Zhou Shaolin Hu School of Automation and Information Engineering Xi’an University of TechnologyXi’anChina School of Automation Foshan UniversityFoshanChina

Purpose–The similarity measurement of time series is an important research in time series detection,which is a basic work of time series clustering,anomaly discovery,prediction and many other data mining *** purpose of this paper is to design a new similarity measurement algorithm to improve the performance of the original similarity measurement *** subsequence morphological information is taken into account by the proposed algorithm,and time series is represented by a pattern,so the similarity measurement algorithm is more ***/methodology/approach–Following some previous researches on similarity measurement,an improved method is *** new method combines morphological representation and dynamic time warping(DTW)technique to measure the similarities of time *** the segmentation of time series data into segments,three parameter values of median,point number and slope are introduced into the improved distance measurement *** effectiveness of the morphological weighted DTW algorithm(MW-DTW)is demonstrated by the example of momentum wheel data of an aircraft attitude control ***–The improved method is insensitive to the distortion and expansion of time axis and can be used to detect the morphological changes of time series *** results confirm that this method proposed in this paper has a high accuracy of similarity *** implications–This improved method has been used to solve the problem of similarity measurement in time series,which is widely emerged in different fields of science and engineering,such as the field of control,measurement,monitoring,process signal processing and economic ***/value–In the similarity measurement of time series,the distance between sequences is often used as the only detection *** results of similarity measurement should not be affected by the longitudinal or transverse stretching and translation changes of the sequence,s

关键词： Dynamic time warping morphological representation Similarity measurement Time series data

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