摘要
An area of Fuzzy time series has attracted increasing interest in the past decade since Song and Chissom's pioneering work and Chen's milestone study. Various enhancements and generalizations have been subsequently proposed, including high-order fuzzy time series. One of the key steps in the Chen's framework is to derive fuzzy relationships existing in a fuzzy time series and to encode the relationships as IF-THEN production rules. A generic exact-match strategy is then applied to the forecasting process. However, the uncertainty and fuzziness characteristics inherent to the fuzzy relationships tend to be overlooked due to the nature of the matching strategies. This omission could lead to inferior forecasting outcomes, particularly in the case of high-order fuzzy time series. In this study, to overcome this shortcoming we propose a best-match strategy forecasting method based on the fuzzy similarity measure. The experiments concerning Taiwan Weighted Stock Index and Dow Jones Industrial Average are reported. We show the effectiveness of the model by running some comparative analysis using some models well-known in the literature.
| 原文 | English |
|---|---|
| 主出版物標題 | Time Series Analysis, Modeling and Applications |
| 主出版物子標題 | A Computational Intelligence Perspective |
| 編輯 | Witold Pedrycz, Shyi-Min Chen |
| 頁面 | 331-345 |
| 頁數 | 15 |
| DOIs | |
| 出版狀態 | Published - 2013 十月 18 |
出版系列
| 名字 | Intelligent Systems Reference Library |
|---|---|
| 卷 | 47 |
| ISSN（列印） | 1868-4394 |
| ISSN（電子） | 1868-4408 |
指紋
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Information Systems and Management
- Library and Information Sciences
引用此文
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A Best-Match Forecasting Model for High-Order Fuzzy Time Series. / Cheng, Yi Chung; Li, Sheng-Tun.
Time Series Analysis, Modeling and Applications: A Computational Intelligence Perspective. 編輯 / Witold Pedrycz; Shyi-Min Chen. 2013. p. 331-345 (Intelligent Systems Reference Library; 卷 47).研究成果: Chapter
TY - CHAP
T1 - A Best-Match Forecasting Model for High-Order Fuzzy Time Series
AU - Cheng, Yi Chung
AU - Li, Sheng-Tun
PY - 2013/10/18
Y1 - 2013/10/18
N2 - An area of Fuzzy time series has attracted increasing interest in the past decade since Song and Chissom's pioneering work and Chen's milestone study. Various enhancements and generalizations have been subsequently proposed, including high-order fuzzy time series. One of the key steps in the Chen's framework is to derive fuzzy relationships existing in a fuzzy time series and to encode the relationships as IF-THEN production rules. A generic exact-match strategy is then applied to the forecasting process. However, the uncertainty and fuzziness characteristics inherent to the fuzzy relationships tend to be overlooked due to the nature of the matching strategies. This omission could lead to inferior forecasting outcomes, particularly in the case of high-order fuzzy time series. In this study, to overcome this shortcoming we propose a best-match strategy forecasting method based on the fuzzy similarity measure. The experiments concerning Taiwan Weighted Stock Index and Dow Jones Industrial Average are reported. We show the effectiveness of the model by running some comparative analysis using some models well-known in the literature.
AB - An area of Fuzzy time series has attracted increasing interest in the past decade since Song and Chissom's pioneering work and Chen's milestone study. Various enhancements and generalizations have been subsequently proposed, including high-order fuzzy time series. One of the key steps in the Chen's framework is to derive fuzzy relationships existing in a fuzzy time series and to encode the relationships as IF-THEN production rules. A generic exact-match strategy is then applied to the forecasting process. However, the uncertainty and fuzziness characteristics inherent to the fuzzy relationships tend to be overlooked due to the nature of the matching strategies. This omission could lead to inferior forecasting outcomes, particularly in the case of high-order fuzzy time series. In this study, to overcome this shortcoming we propose a best-match strategy forecasting method based on the fuzzy similarity measure. The experiments concerning Taiwan Weighted Stock Index and Dow Jones Industrial Average are reported. We show the effectiveness of the model by running some comparative analysis using some models well-known in the literature.
UR - http://www.scopus.com/inward/record.url?scp=84885436409&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84885436409&partnerID=8YFLogxK
U2 - 10.1007/1007/978-3-642-33439-9_15
DO - 10.1007/1007/978-3-642-33439-9_15
M3 - Chapter
AN - SCOPUS:84885436409
SN - 9783642334382
T3 - Intelligent Systems Reference Library
SP - 331
EP - 345
BT - Time Series Analysis, Modeling and Applications
A2 - Pedrycz, Witold
A2 - Chen, Shyi-Min
ER -