Article View/Open
Publication Export
Related Publications in TAIR
- > Simple Record
- > Full Record
| Field |
Value |
| Title: | Use of fuzzy statistical technique in change periods detection of nonlinear time series. |
| Authors: | 吳柏林
Wu, Berlin
Chen, Mei-Hui |
| Contributors: | 應數系 |
| Keywords: | Change periods; Nonlinear time series; Revised centered cumulative sums of squares (RCUSUM); Fuzzy statistics |
| Date: | 1999-03 |
| Issue Date: | 2018-09-28 16:35:07 (UTC+8) |
| Abstract: | Many papers have been presented on the study of change points detection. Nonetheless, we would like to point out that in dealing with the time series with switching regimes, we should also take the characteristics of change periods into account. Because many patterns of change structure in time series exhibit a certain kind of duration, those phenomena should not be treated as a mere sudden turning at a certain time. In this paper, we propose a procedure about change periods detection for nonlinear time series. The detecting statistical method is an application of fuzzy classification and a generalization of Inclan and Tiao's result [J. Am. Statist. Assoc. 89 (1994) 913]. Simulation results show that the performance of the proposed procedure is efficient and successful. Finally, an empirical application about change periods detecting for Taiwan monthly visitor's arrival is demonstrated. |
| Relation: | Applied Mathematics and Computation, Volume 99, Issues 2-3, Pages 241-254
AMS MathSciNet:MR1663961 |
| Data Type: | article |
| DOI: | https://doi.org/10.1016/S0096-3003(98)00011-3 |
| DCField |
Value |
Language |
| dc.contributor (Contributor) | 應數系 | |
| dc.creator (Authors) | 吳柏林 | zh_TW |
| dc.creator (Authors) | Wu, Berlin | en_US |
| dc.creator (Authors) | Chen, Mei-Hui | en_US |
| dc.date (Date) | 1999-03 | |
| dc.date.accessioned | 2018-09-28 16:35:07 (UTC+8) | - |
| dc.date.available | 2018-09-28 16:35:07 (UTC+8) | - |
| dc.date.issued (Issue Date) | 2018-09-28 16:35:07 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/120198 | - |
| dc.description.abstract (Abstract) | Many papers have been presented on the study of change points detection. Nonetheless, we would like to point out that in dealing with the time series with switching regimes, we should also take the characteristics of change periods into account. Because many patterns of change structure in time series exhibit a certain kind of duration, those phenomena should not be treated as a mere sudden turning at a certain time. In this paper, we propose a procedure about change periods detection for nonlinear time series. The detecting statistical method is an application of fuzzy classification and a generalization of Inclan and Tiao's result [J. Am. Statist. Assoc. 89 (1994) 913]. Simulation results show that the performance of the proposed procedure is efficient and successful. Finally, an empirical application about change periods detecting for Taiwan monthly visitor's arrival is demonstrated. | en_US |
| dc.format.extent | 647879 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (Relation) | Applied Mathematics and Computation, Volume 99, Issues 2-3, Pages 241-254 | |
| dc.relation (Relation) | AMS MathSciNet:MR1663961 | |
| dc.subject (Keywords) | Change periods; Nonlinear time series; Revised centered cumulative sums of squares (RCUSUM); Fuzzy statistics | en_US |
| dc.title (Title) | Use of fuzzy statistical technique in change periods detection of nonlinear time series. | en_US |
| dc.type (Data Type) | article | |
| dc.identifier.doi (DOI) | 10.1016/S0096-3003(98)00011-3 | |
| dc.doi.uri | https://doi.org/10.1016/S0096-3003(98)00011-3 | |
(234)