A New Time Series Similarity Measurement Method Based on Fluctuation Features
Time series similarity measurement is one of the fundamental tasks in time series data mining, and there are many studies on time series similarity measurement methods. However, the majority of them only calculate the distance between equal-length time series, and also cannot adequately reflect the...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2020-01-01
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| Series: | Tehnički Vjesnik |
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| Online Access: | https://hrcak.srce.hr/file/351984 |
| _version_ | 1797207195280998400 |
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| author | Hailan Chen Xuedong Gao |
| author_facet | Hailan Chen Xuedong Gao |
| author_sort | Hailan Chen |
| collection | DOAJ |
| description | Time series similarity measurement is one of the fundamental tasks in time series data mining, and there are many studies on time series similarity measurement methods. However, the majority of them only calculate the distance between equal-length time series, and also cannot adequately reflect the fluctuation features of time series. To solve this problem, a new time series similarity measurement method based on fluctuation features is proposed in this paper. Firstly, the fluctuation features extraction method of time series is introduced. By defining and identifying fluctuation points, the fluctuation points sequence is obtained to represent the original time series for subsequent analysis. Then, a new similarity measurement (D_SM) is put forward to calculate the distance between different fluctuation points sequences. This method can calculate the distance of unequal-length time series, and it includes two main steps: similarity matching and the distance calculation based on similarity matching. Finally, the experiments are performed on some public time series using agglomerative hierarchical clustering based on D_SM. Compared to some traditional time series similarity measurements, the clustering results show that the proposed method can effectively distinguish time series with similar shapes from different classes and get a visible improvement in clustering accuracy in terms of F-Measure. |
| first_indexed | 2024-04-24T09:19:03Z |
| format | Article |
| id | doaj.art-2ea3cc7a7ee84aeca20df46ba46d8121 |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:19:03Z |
| publishDate | 2020-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-2ea3cc7a7ee84aeca20df46ba46d81212024-04-15T16:22:31ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392020-01-012741134114110.17559/TV-20200107171121A New Time Series Similarity Measurement Method Based on Fluctuation FeaturesHailan Chen0Xuedong Gao1Donlinks School of Economics and Management, University of Science and Technology Beijing, No. 30 Xueyuan Road, Haidian District, Beijing, ChinaDonlinks School of Economics and Management, University of Science and Technology Beijing, No. 30 Xueyuan Road, Haidian District, Beijing, ChinaTime series similarity measurement is one of the fundamental tasks in time series data mining, and there are many studies on time series similarity measurement methods. However, the majority of them only calculate the distance between equal-length time series, and also cannot adequately reflect the fluctuation features of time series. To solve this problem, a new time series similarity measurement method based on fluctuation features is proposed in this paper. Firstly, the fluctuation features extraction method of time series is introduced. By defining and identifying fluctuation points, the fluctuation points sequence is obtained to represent the original time series for subsequent analysis. Then, a new similarity measurement (D_SM) is put forward to calculate the distance between different fluctuation points sequences. This method can calculate the distance of unequal-length time series, and it includes two main steps: similarity matching and the distance calculation based on similarity matching. Finally, the experiments are performed on some public time series using agglomerative hierarchical clustering based on D_SM. Compared to some traditional time series similarity measurements, the clustering results show that the proposed method can effectively distinguish time series with similar shapes from different classes and get a visible improvement in clustering accuracy in terms of F-Measure.https://hrcak.srce.hr/file/351984clusteringfluctuation featuressimilarity measurementtime series |
| spellingShingle | Hailan Chen Xuedong Gao A New Time Series Similarity Measurement Method Based on Fluctuation Features Tehnički Vjesnik clustering fluctuation features similarity measurement time series |
| title | A New Time Series Similarity Measurement Method Based on Fluctuation Features |
| title_full | A New Time Series Similarity Measurement Method Based on Fluctuation Features |
| title_fullStr | A New Time Series Similarity Measurement Method Based on Fluctuation Features |
| title_full_unstemmed | A New Time Series Similarity Measurement Method Based on Fluctuation Features |
| title_short | A New Time Series Similarity Measurement Method Based on Fluctuation Features |
| title_sort | new time series similarity measurement method based on fluctuation features |
| topic | clustering fluctuation features similarity measurement time series |
| url | https://hrcak.srce.hr/file/351984 |
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