Determination of temporal information granules to improve forecasting in fuzzy time series |
| |
| Affiliation: | 1. Research Center of Information and Control, Dalian University of Technology, Dalian 116024, China;2. School of Mathematics and System Science, Shenyang Normal University, Shenyang 110034, China;3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6R 2V4, AB, Canada;1. Biomedical Knowledge Engineering Laboratory, Seoul National University, Republic of Korea;2. Dental Research Institute, Seoul National University, Republic of Korea;3. Institute of Human-Environment Interface Biology, Seoul National University, Republic of Korea;1. Department of Computer Science and Engineering, University of Dhaka, Bangladesh;2. Department of Computer Engineering, Kyung Hee University, South Korea;1. School of Management, Fuzhou University, Fuzhou, Fujian 350108, China;2. College of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China;1. College of Computer Science and Technology, Key Laboratory of Symbolic Computing and Knowledge Engineering of Ministry of Education, Jilin University, 130012 Changchun, China;2. College of Computer Science and Engineering, Changchun University of Technology, 130012 Changchun, China |
| |
| Abstract: | Partitioning the universe of discourse and determining intervals containing useful temporal information and coming with better interpretability are critical for forecasting in fuzzy time series. In the existing literature, researchers seldom consider the effect of time variable when they partition the universe of discourse. As a result, and there is a lack of interpretability of the resulting temporal intervals. In this paper, we take the temporal information into account to partition the universe of discourse into intervals with unequal length. As a result, the performance improves forecasting quality. First, time variable is involved in partitioning the universe through Gath–Geva clustering-based time series segmentation and obtain the prototypes of data, then determine suitable intervals according to the prototypes by means of information granules. An effective method of partitioning and determining intervals is proposed. We show that these intervals carry well-defined semantics. To verify the effectiveness of the approach, we apply the proposed method to forecast enrollment of students of Alabama University and the Taiwan Stock Exchange Capitalization Weighted Stock Index. The experimental results show that the partitioning with temporal information can greatly improve accuracy of forecasting. Furthermore, the proposed method is not sensitive to its parameters. |
| |
| Keywords: | Fuzzy time series Gath–Geva (GG) clustering Information granules Enrollment Segmentation |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
