Unsupervised Change Point Detection and Trend Prediction for Financial Time-Series Using a New CUSUM-Based Approach
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Kyungwon | - |
dc.contributor.author | Park, Ji Hwan | - |
dc.contributor.author | Lee, Minhyuk | - |
dc.contributor.author | Song, Jae Wook | - |
dc.date.accessioned | 2022-07-06T06:22:26Z | - |
dc.date.available | 2022-07-06T06:22:26Z | - |
dc.date.created | 2022-05-04 | - |
dc.date.issued | 2022-04 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/138930 | - |
dc.description.abstract | The aim of this research is to propose a binary segmentation algorithm to detect the change points in financial time-series based on the Iterative Cumulative Sum of Squares (ICSS). The proposed algorithm, entitled KW-ICSS, utilizes the non-parametric Kruskal-Wallis test in cross-validation procedures. In this regard, KW-ICSS can quickly detect the change points in non-normally distributed time-series with a small number of observations after the change points than the state-of-the-art ICSS algorithm, entitled AIT-ICSS. For the simulated financial time-series whose true location of the change point is known, KW-ICSS detects the change points with the average true positive rate of 81% for the different number of change points, whereas AIT-ICSS only exhibits 72.57%. Also, KW-ICSS's mean absolute deviation between the true and detected change points is less than that of AIT-ICSS for different significance levels. The experiment also finds that the significance level, the model parameter, should be set to less than 10%. For the real-world financial time-series whose true location of change points is unknown, KW-ICSS's robust detection of change points is observed from fewer detected change points and longer intervals between them. Furthermore, KW-ICSS's trend prediction for the short-term future performs with an average of 92.47% accuracy, whereas AIT-ICSS shows 90.69%. Therefore, we claim that KW-ICSS successfully improves AIT-ICSS. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Unsupervised Change Point Detection and Trend Prediction for Financial Time-Series Using a New CUSUM-Based Approach | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Song, Jae Wook | - |
dc.identifier.doi | 10.1109/ACCESS.2022.3162399 | - |
dc.identifier.scopusid | 2-s2.0-85128464126 | - |
dc.identifier.wosid | 000778877300001 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.10, pp.34690 - 34705 | - |
dc.relation.isPartOf | IEEE ACCESS | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 10 | - |
dc.citation.startPage | 34690 | - |
dc.citation.endPage | 34705 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | STRUCTURAL-CHANGES | - |
dc.subject.keywordPlus | VARIANCE CHANGE | - |
dc.subject.keywordPlus | OIL PRICES | - |
dc.subject.keywordPlus | VOLATILITY | - |
dc.subject.keywordPlus | BREAKS | - |
dc.subject.keywordAuthor | Prediction algorithms | - |
dc.subject.keywordAuthor | Market research | - |
dc.subject.keywordAuthor | Robustness | - |
dc.subject.keywordAuthor | Estimation | - |
dc.subject.keywordAuthor | Bayes methods | - |
dc.subject.keywordAuthor | Data models | - |
dc.subject.keywordAuthor | Unsupervised learning | - |
dc.subject.keywordAuthor | Unsupervised learning | - |
dc.subject.keywordAuthor | change point detection | - |
dc.subject.keywordAuthor | iterative cumulative sum of squares | - |
dc.subject.keywordAuthor | Kruskal-Wallis | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9741807 | - |
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