Natural partitioning-based forecasting model for fuzzy time-series

Sheng-Tun Li, Yeh Peng Chen

研究成果: Conference contribution

27 引文 斯高帕斯(Scopus)

摘要

Since the forecasting framework of fuzzy time-series introduced, there have been a variety of models developed to improve forecasting accuracy or reduce computation overhead. However, the issue of partitioning intervals has rarely been investigated. This paper presents a novel approach to handling the issue by applying the natural partitioning technique, which can recursively partition the universe of discourse level by level in a natural way. Experimental results on the enrollment data of the University of Alabama demonstrate that the resulting forecasting model can forecast the data effectively and efficiently and outperforms the existing models. Furthermore, the propose model can be extended to handle high-order fuzzy time series.

原文English
主出版物標題2004 IEEE International Conference on Fuzzy Systems - Proceedings
頁面1355-1359
頁數5
DOIs
出版狀態Published - 2004 十二月 1
事件2004 IEEE International Conference on Fuzzy Systems - Proceedings - Budapest, Hungary
持續時間: 2004 七月 252004 七月 29

出版系列

名字IEEE International Conference on Fuzzy Systems
3
ISSN(列印)1098-7584

Other

Other2004 IEEE International Conference on Fuzzy Systems - Proceedings
國家Hungary
城市Budapest
期間04-07-2504-07-29

    指紋

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

引用此

Li, S-T., & Chen, Y. P. (2004). Natural partitioning-based forecasting model for fuzzy time-series. 於 2004 IEEE International Conference on Fuzzy Systems - Proceedings (頁 1355-1359). (IEEE International Conference on Fuzzy Systems; 卷 3). https://doi.org/10.1109/FUZZY.2004.1375366