Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)

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

Authors
Chengguo Yin, Hongxu Wang, Hao Feng, Xiaoli Lu
Corresponding Author
Chengguo Yin
Available Online March 2017.
DOI
https://doi.org/10.2991/msam-17.2017.10How to use a DOI?
Keywords
fuzzy time series forecasting method; fuzzy number function of SIFBODR; inverse fuzzy number function of SIFBODR; forecasting function of SIFBODR
Abstract
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.
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Proceedings
2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)
Part of series
Advances in Intelligent Systems Research
Publication Date
March 2017
ISBN
978-94-6252-324-1
ISSN
1951-6851
DOI
https://doi.org/10.2991/msam-17.2017.10How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

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  - 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  - https://doi.org/10.2991/msam-17.2017.10
ID  - Yin2017/03
ER  -