Direct least squares fitting of ellipses segmentation and prioritized rules classification for curve-shaped chart patterns
In financial markets, appearances of chart patterns in time series are commonly considered as potential signals for imminent change in the direction of price movement. To identify chart patterns, time series data is usually segmented before it can be processed by different classification methods. Ho...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021
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| Subjects: | |
| Online Access: | http://eprints.sunway.edu.my/1746/ http://eprints.sunway.edu.my/1746/1/Teh%20Phoey%20Lee%20Direct%20least%20squares%20fitting.pdf |
| _version_ | 1848802125110312960 |
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| author | Lei, I. L. Teh, Phoey Lee * Si, Yain-Whar |
| author_facet | Lei, I. L. Teh, Phoey Lee * Si, Yain-Whar |
| author_sort | Lei, I. L. |
| building | SU Institutional Repository |
| collection | Online Access |
| description | In financial markets, appearances of chart patterns in time series are commonly considered as potential signals for imminent change in the direction of price movement. To identify chart patterns, time series data is usually segmented before it can be processed by different classification methods. However, existing segmentation methods are less effective in classifying 16 curve-shaped chart patterns from financial time series. In this paper, we propose three novel segmentation methods for classification of curveshaped chart patterns based on direct least squares fitting of ellipses. These methods are implemented based on the principles of sliding windows, turning points, and bottom-up piece wise linear approximation. To further enhance the efficiency of classifying chart patterns from real-time streaming data, we propose a novel algorithm called Accelerating Classification with Prioritized Rules (ACPR). Experiments based on real datasets from financial markets reveal that the proposed approaches are effective in classifying curveshaped patterns from time series. Experiment results reveal that the proposed segmentation methods with
ACPR can significantly reduce the total execution time. |
| first_indexed | 2025-11-14T21:18:22Z |
| format | Article |
| id | sunway-1746 |
| institution | Sunway University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T21:18:22Z |
| publishDate | 2021 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | sunway-17462021-04-22T03:46:02Z http://eprints.sunway.edu.my/1746/ Direct least squares fitting of ellipses segmentation and prioritized rules classification for curve-shaped chart patterns Lei, I. L. Teh, Phoey Lee * Si, Yain-Whar QA75 Electronic computers. Computer science In financial markets, appearances of chart patterns in time series are commonly considered as potential signals for imminent change in the direction of price movement. To identify chart patterns, time series data is usually segmented before it can be processed by different classification methods. However, existing segmentation methods are less effective in classifying 16 curve-shaped chart patterns from financial time series. In this paper, we propose three novel segmentation methods for classification of curveshaped chart patterns based on direct least squares fitting of ellipses. These methods are implemented based on the principles of sliding windows, turning points, and bottom-up piece wise linear approximation. To further enhance the efficiency of classifying chart patterns from real-time streaming data, we propose a novel algorithm called Accelerating Classification with Prioritized Rules (ACPR). Experiments based on real datasets from financial markets reveal that the proposed approaches are effective in classifying curveshaped patterns from time series. Experiment results reveal that the proposed segmentation methods with ACPR can significantly reduce the total execution time. Elsevier 2021 Article PeerReviewed text en cc_by_nc_4 http://eprints.sunway.edu.my/1746/1/Teh%20Phoey%20Lee%20Direct%20least%20squares%20fitting.pdf Lei, I. L. and Teh, Phoey Lee * and Si, Yain-Whar (2021) Direct least squares fitting of ellipses segmentation and prioritized rules classification for curve-shaped chart patterns. Applied Soft Computing, 107. p. 107363. ISSN 1568-4946 http://doi.org/10.1016/j.asoc.2021.107363 doi:10.1016/j.asoc.2021.107363 |
| spellingShingle | QA75 Electronic computers. Computer science Lei, I. L. Teh, Phoey Lee * Si, Yain-Whar Direct least squares fitting of ellipses segmentation and prioritized rules classification for curve-shaped chart patterns |
| title | Direct least squares fitting of ellipses segmentation and prioritized rules classification for curve-shaped chart patterns |
| title_full | Direct least squares fitting of ellipses segmentation and prioritized rules classification for curve-shaped chart patterns |
| title_fullStr | Direct least squares fitting of ellipses segmentation and prioritized rules classification for curve-shaped chart patterns |
| title_full_unstemmed | Direct least squares fitting of ellipses segmentation and prioritized rules classification for curve-shaped chart patterns |
| title_short | Direct least squares fitting of ellipses segmentation and prioritized rules classification for curve-shaped chart patterns |
| title_sort | direct least squares fitting of ellipses segmentation and prioritized rules classification for curve-shaped chart patterns |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.sunway.edu.my/1746/ http://eprints.sunway.edu.my/1746/ http://eprints.sunway.edu.my/1746/ http://eprints.sunway.edu.my/1746/1/Teh%20Phoey%20Lee%20Direct%20least%20squares%20fitting.pdf |