The Set of Fuzzy Time Series Forecasting Models Based on the Ordered Difference Rate
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- fuzzy time series forecasting method; fuzzy number function of SFBODR; inverse fuzzy number function of SFBODR; forecasting function of SFBODR
Song and Chissom established fuzzy time series forecasting model in 1993. Stevenson and Porter improved the forecasting model of Jilani, Burney, and Ardil in 2009, and researched the forecasting problem of enrollments of the University of Alabama 1971–1992. Although they obtained the best prediction accuracy by 2009, the prediction accuracy was still not ideal. In this paper, we improved the forecasting model of Stevenson and Porter, and got the SFBODR (The Set of Fuzzy Time Series Forecasting Models Based on the Ordered Difference Rate). The forecasting model SFBODR(0.00004, 0.00003) can get the ideal state of AFER(Average Forecasting Error Rate) = 0% and MSE(Mean Square Error) = 0 in forecasting the enrollments of the University of Alabama.
- © 2017, the Authors. Published by Atlantis Press.
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Cite this article
TY - CONF AU - Chengguo Yin AU - Hongxu Wang AU - Hao Feng AU - Xiaoli Lu PY - 2017/03 DA - 2017/03 TI - The Set of Fuzzy Time Series Forecasting Models Based on the Ordered Difference Rate BT - Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017) PB - Atlantis Press SP - 34 EP - 37 SN - 1951-6851 UR - https://doi.org/10.2991/msam-17.2017.9 DO - https://doi.org/10.2991/msam-17.2017.9 ID - Yin2017/03 ER -