Set of Fuzzy Time Series Forecasting Models Based on the Difference Rate
Xiaojing Zhu, Hongxu Wang, Chengguo Yin, Xiaoli Lu
Available Online March 2017.
- 10.2991/msam-17.2017.13How to use a DOI?
- fuzzy time series forecasting method; SFBDR fuzzy number function; SFBDR inverse fuzzy number function; SFBDR Predicted function
Song & Chissom introduced the concept of fuzzy time series in 1993, and many fuzzy time series methods have been proposed, however, the prediction accuracy is not high, among which, Jilani, Burney and Ardil (2007) proposed prediction model has achieved a high accuracy. This paper improves their predicted model, and proposed the set of fuzzy time series forecasting models Based on the difference rate, simplified as SFBDR, it contains the predicted model SFBDR (0.000001, 0.000003) and SFBDR (0.000003, 0.000001), in the historical enrollment of University of Alabama it can get the highest predicted accuracy of AFER=0% and MSE=0.
- © 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 - Xiaojing Zhu AU - Hongxu Wang AU - Chengguo Yin AU - Xiaoli Lu PY - 2017/03 DA - 2017/03 TI - Set of Fuzzy Time Series Forecasting Models Based on the Difference Rate BT - Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017) PB - Atlantis Press SP - 49 EP - 52 SN - 1951-6851 UR - https://doi.org/10.2991/msam-17.2017.13 DO - 10.2991/msam-17.2017.13 ID - Zhu2017/03 ER -