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

DOI

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.

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 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  -