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| Title | Use of Partial Cumulative Sum to Detect Trends and Change Periods for Nonlinear Time Series |
| Creator | Wu, Berlin 吳柏林 Chen, Liyang 陳力揚 |
| Contributor | 應數系 |
| Key Words | fuzzy time series;change periods;partial cumulative sums;trend;noise |
| Date | 2006-07 |
| Date Issued | 2-Oct-2015 16:50:49 (UTC+8) |
| Summary | Because the structural change of a time series from one pattern to another may not switch at once but rather experience a period of adjustment, conventional change point detection may be inappropriate under some circumstances. Furthermore, changes in time series often occur gradually so that there is a certain amount of fuzziness in the change point. For this, considerable research has focused on the theory of change period detection for improved model performance. However, a change period in some small time interval may appear to be negligible noise in a larger time interval. In this paper, we propose an approach to detect trends and change periods with fuzzy statistics using partial cumulative sums. By controlling the parameters, we can filter the noises and discover suitable change periods. Having discovered the change periods, we can proceed to identify the trends in the time series. We use simulations to test our approach. Our results show that the performance of our approach is satisfactory. |
| Relation | 經濟與管理論叢(Journal of Economics and Management), 2(2), 123-145 |
| Type | article |
| dc.contributor | 應數系 | - |
| dc.creator (作者) | Wu, Berlin | - |
| dc.creator (作者) | 吳柏林 | zh_TW |
| dc.creator (作者) | Chen, Liyang | en_US |
| dc.creator (作者) | 陳力揚 | zh_TW |
| dc.date (日期) | 2006-07 | - |
| dc.date.accessioned | 2-Oct-2015 16:50:49 (UTC+8) | - |
| dc.date.available | 2-Oct-2015 16:50:49 (UTC+8) | - |
| dc.date.issued (上傳時間) | 2-Oct-2015 16:50:49 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/78859 | - |
| dc.description.abstract (摘要) | Because the structural change of a time series from one pattern to another may not switch at once but rather experience a period of adjustment, conventional change point detection may be inappropriate under some circumstances. Furthermore, changes in time series often occur gradually so that there is a certain amount of fuzziness in the change point. For this, considerable research has focused on the theory of change period detection for improved model performance. However, a change period in some small time interval may appear to be negligible noise in a larger time interval. In this paper, we propose an approach to detect trends and change periods with fuzzy statistics using partial cumulative sums. By controlling the parameters, we can filter the noises and discover suitable change periods. Having discovered the change periods, we can proceed to identify the trends in the time series. We use simulations to test our approach. Our results show that the performance of our approach is satisfactory. | - |
| dc.format.extent | 249367 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | 經濟與管理論叢(Journal of Economics and Management), 2(2), 123-145 | - |
| dc.subject (關鍵詞) | fuzzy time series;change periods;partial cumulative sums;trend;noise | - |
| dc.title (題名) | Use of Partial Cumulative Sum to Detect Trends and Change Periods for Nonlinear Time Series | - |
| dc.type (資料類型) | article | en |