The Set of Improved Fuzzy Time Series Forecasting Models Based on the Ordered Difference Rate
Chengguo Yin, Hongxu Wang, Hao Feng, Xiaoli Lu
Available Online March 2017.
- 10.2991/msam-17.2017.10How to use a DOI?
- fuzzy time series forecasting method; fuzzy number function of SIFBODR; inverse fuzzy number function of SIFBODR; forecasting function of SIFBODR
Song and Chissom first proposed the fuzzy time series forecasting model in 1993. In this paper, we improved the forecasting model proposed by Stevenson and Porter, and dug out the SIFBODR (The Set of Improved Fuzzy Time Series Forecasting Models Based on the Ordered Difference Rate). In the research on the forecasting problem of enrollments of the University of Alabama 1971–1992, the forecasting model SIFBODR(0.00002, 0.00004) of SIFBODR can obtain AFER (Average Forecasting Error Rate) = 0% and MSE(Mean Square Error) = 0. The problem that the prediction accuracy of fuzzy time series forecasting models is not high for many years is basically solved.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
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 Improved 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 - 38 EP - 41 SN - 1951-6851 UR - https://doi.org/10.2991/msam-17.2017.10 DO - 10.2991/msam-17.2017.10 ID - Yin2017/03 ER -