Applying Grey Relational Analysis to Detect Change Points in Time Series
The goal of detecting change points is to recognize abrupt changes in time series data. This is suitable, for instance, to find events that characterize the financial market or to inspect data streams of stock returns. Regression models categorized as supervised methods have played a significant rol...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2022/9242773 |
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| _version_ | 1850222324130775040 |
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| author | Yi-Chung Hu Shu-hen Chiang Yu-Jing Chiu |
| author_facet | Yi-Chung Hu Shu-hen Chiang Yu-Jing Chiu |
| author_sort | Yi-Chung Hu |
| collection | DOAJ |
| description | The goal of detecting change points is to recognize abrupt changes in time series data. This is suitable, for instance, to find events that characterize the financial market or to inspect data streams of stock returns. Regression models categorized as supervised methods have played a significant role in change-point detection. However, since change points might not be available beforehand to train the model, and because the series data might be statistically atypical, the applicability of regression models is limited. To avoid statistical assumptions, this study uses the grey theory, a kind of artificial intelligence tools, to measure the relationships between sequences by grey relational analysis (GRA). This paper contributes to propose an unsupervised method to detect possible change points in time series by GRA. Change-point analysis of the proposed method was performed on S&P100 stock returns. Experimental results from evaluating the recognition accuracy rate show that the proposed method performs well compared to other methods considered for change-point detection. |
| format | Article |
| id | doaj-art-6f393b7ebead414e8d69bff9814a77df |
| institution | OA Journals |
| issn | 2314-4785 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-6f393b7ebead414e8d69bff9814a77df2025-08-20T02:06:23ZengWileyJournal of Mathematics2314-47852022-01-01202210.1155/2022/9242773Applying Grey Relational Analysis to Detect Change Points in Time SeriesYi-Chung Hu0Shu-hen Chiang1Yu-Jing Chiu2Department of Business AdministrationDepartment of FinanceDepartment of Business AdministrationThe goal of detecting change points is to recognize abrupt changes in time series data. This is suitable, for instance, to find events that characterize the financial market or to inspect data streams of stock returns. Regression models categorized as supervised methods have played a significant role in change-point detection. However, since change points might not be available beforehand to train the model, and because the series data might be statistically atypical, the applicability of regression models is limited. To avoid statistical assumptions, this study uses the grey theory, a kind of artificial intelligence tools, to measure the relationships between sequences by grey relational analysis (GRA). This paper contributes to propose an unsupervised method to detect possible change points in time series by GRA. Change-point analysis of the proposed method was performed on S&P100 stock returns. Experimental results from evaluating the recognition accuracy rate show that the proposed method performs well compared to other methods considered for change-point detection.http://dx.doi.org/10.1155/2022/9242773 |
| spellingShingle | Yi-Chung Hu Shu-hen Chiang Yu-Jing Chiu Applying Grey Relational Analysis to Detect Change Points in Time Series Journal of Mathematics |
| title | Applying Grey Relational Analysis to Detect Change Points in Time Series |
| title_full | Applying Grey Relational Analysis to Detect Change Points in Time Series |
| title_fullStr | Applying Grey Relational Analysis to Detect Change Points in Time Series |
| title_full_unstemmed | Applying Grey Relational Analysis to Detect Change Points in Time Series |
| title_short | Applying Grey Relational Analysis to Detect Change Points in Time Series |
| title_sort | applying grey relational analysis to detect change points in time series |
| url | http://dx.doi.org/10.1155/2022/9242773 |
| work_keys_str_mv | AT yichunghu applyinggreyrelationalanalysistodetectchangepointsintimeseries AT shuhenchiang applyinggreyrelationalanalysistodetectchangepointsintimeseries AT yujingchiu applyinggreyrelationalanalysistodetectchangepointsintimeseries |