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

Published: 01 March 2007 Publication History

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.

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  • (2023)Spatio-Temporal Trajectory Similarity Measures: A Comprehensive Survey and Quantitative StudyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.332353536:5(2191-2212)Online publication date: 10-Oct-2023
  • (2019)Study on Data Transfer in Meteorological Forecast of Small and Medium-Sized Cities and Its Application in Zhaoqing CityThe Computer Journal10.1093/comjnl/bxz087Online publication date: 10-Sep-2019
  • (2019)A survey of trajectory distance measures and performance evaluationThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-019-00574-929:1(3-32)Online publication date: 18-Oct-2019
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Published In

cover image ACM SIGMOD Record
ACM SIGMOD Record  Volume 36, Issue 1
March 2007
60 pages
ISSN:0163-5808
DOI:10.1145/1276301
Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 March 2007
Published in SIGMOD Volume 36, Issue 1

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Cited By

View all
  • (2023)Spatio-Temporal Trajectory Similarity Measures: A Comprehensive Survey and Quantitative StudyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.332353536:5(2191-2212)Online publication date: 10-Oct-2023
  • (2019)Study on Data Transfer in Meteorological Forecast of Small and Medium-Sized Cities and Its Application in Zhaoqing CityThe Computer Journal10.1093/comjnl/bxz087Online publication date: 10-Sep-2019
  • (2019)A survey of trajectory distance measures and performance evaluationThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-019-00574-929:1(3-32)Online publication date: 18-Oct-2019
  • (2011)On nonmetric similarity search problems in complex domainsACM Computing Surveys10.1145/1978802.197881343:4(1-50)Online publication date: 18-Oct-2011
  • (2011)Non-metric similarity search problems in very large collectionsProceedings of the 2011 IEEE 27th International Conference on Data Engineering10.1109/ICDE.2011.5767955(1362-1365)Online publication date: 11-Apr-2011

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