The Set of Fuzzy Time Series Forecasting Models Based on the Ordered Difference Rate

DOI

10.2991/msam-17.2017.9How to use a DOI?

Keywords

fuzzy time series forecasting method; fuzzy number function of SFBODR; inverse fuzzy number function of SFBODR; forecasting function of SFBODR

Abstract

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.

Copyright

© 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/).

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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  - 10.2991/msam-17.2017.9
ID  - Yin2017/03
ER  -