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General Hierarchical Model (GHM) to measure similarity of time series

General Hierarchical Model (GHM) to measure similarity of time series Similarity query is a frequent subroutine in time series database to find the similar time series of the given one. In this process, similarity measure plays a very important part. The previous methods do not consider the relation between point correspondences and the importance (role) of the points on the content of time series during measuring similarity, resulting in their low accuracies in many real applications. In the paper, we propose a General Hierarchical Model (GHM), which determines the point correspondences by the hierarchies of points. It partitions the points of time series into different hierarchies, and then the points are restricted to be compared with the ones in the same hierarchy. The practical methods can be implemented based on the model with any real requirements, e.g. FFT Hierarchical Measures (FHM) given in this paper. And the hierarchical filtering methods of GHM are provided for range and k -NN queries respectively. Finally, two common data sets were used in k -NN query and clustering experiments to test the effectiveness of our approach and others. The time performance comparisons of all the tested methods were performed using the synthetic data set with various sizes. The experimental results show the superiority of our approach over the competitors. And we also give the experimental powers of the filtering methods proposed in the queries. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGMOD Record Association for Computing Machinery

General Hierarchical Model (GHM) to measure similarity of time series

ACM SIGMOD Record , Volume 36 (1) – Mar 1, 2007

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References (20)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2007 by ACM Inc.
ISSN
0163-5808
DOI
10.1145/1276301.1276304
Publisher site
See Article on Publisher Site

Abstract

Similarity query is a frequent subroutine in time series database to find the similar time series of the given one. In this process, similarity measure plays a very important part. The previous methods do not consider the relation between point correspondences and the importance (role) of the points on the content of time series during measuring similarity, resulting in their low accuracies in many real applications. In the paper, we propose a General Hierarchical Model (GHM), which determines the point correspondences by the hierarchies of points. It partitions the points of time series into different hierarchies, and then the points are restricted to be compared with the ones in the same hierarchy. The practical methods can be implemented based on the model with any real requirements, e.g. FFT Hierarchical Measures (FHM) given in this paper. And the hierarchical filtering methods of GHM are provided for range and k -NN queries respectively. Finally, two common data sets were used in k -NN query and clustering experiments to test the effectiveness of our approach and others. The time performance comparisons of all the tested methods were performed using the synthetic data set with various sizes. The experimental results show the superiority of our approach over the competitors. And we also give the experimental powers of the filtering methods proposed in the queries.

Journal

ACM SIGMOD RecordAssociation for Computing Machinery

Published: Mar 1, 2007

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