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

DOI

10.2991/msam-17.2017.13How to use a DOI?

Keywords

fuzzy time series forecasting method; SFBDR fuzzy number function; SFBDR inverse fuzzy number function; SFBDR Predicted function

Abstract

Song & Chissom introduced the concept of fuzzy time series in 1993[1], 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.

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  - 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  -