Title :
Natural partitioning-based forecasting model for fuzzy time-series
Author :
Li, Sheng-Tun ; Chen, Yeh-peng
Author_Institution :
Inst. of Inf. Manage., Nat. Cheng Kung Univ., Tainan, Taiwan
Abstract :
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 work 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 proposed model can be extended to handle high-order fuzzy time series.
Keywords :
forecasting theory; fuzzy set theory; time series; computation overhead reduction; fuzzy time-series forecasting framework; natural partitioning-based forecasting model; Air pollution; Economic forecasting; Fuzzy logic; Fuzzy reasoning; Fuzzy sets; Information management; Monitoring; Predictive models; Protection; Stock markets;
Conference_Titel :
Fuzzy Systems, 2004. Proceedings. 2004 IEEE International Conference on
Print_ISBN :
0-7803-8353-2
DOI :
10.1109/FUZZY.2004.1375366