Proceedings of the 2016 International Conference on Education, Management and Computer Science

Fuzzy Time Series Forecasting Model of Inverse Fuzzy Number Based on Percentage Year by Year of Continuous Point

Authors
Haifeng Wang, Kun Zhang, Zhuang Li, Hongxu Wang
Corresponding Author
Haifeng Wang
Available Online May 2016.
DOI
https://doi.org/10.2991/icemc-16.2016.58How to use a DOI?
Keywords
Percentage; Continuous point; Inverse fuzzy number; Fuzzy time-series forecasting
Abstract
Fuzzy time-series forecasting model of inverse fuzzy number based on percentage year to year of continuous point is proposed. We improved the forecasting model of Saxena. The new model puts the percentage year to year of historical data as for the domain of discourse, uses percentage year to year of continuous point to define fuzzy number, and then defines the corresponding inverse fuzzy number. At last we again provide the predictor formula and study the prediction problem of freshman registration number at the University of Alabama, in order to demonstrate the application of a new prediction model. The results show that AFER and MSE of the model are very small compared with the existing models.
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Volume Title
Proceedings of the 2016 International Conference on Education, Management and Computer Science
Series
Advances in Intelligent Systems Research
Publication Date
May 2016
ISBN
978-94-6252-202-2
ISSN
1951-6851
DOI
https://doi.org/10.2991/icemc-16.2016.58How 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  - Haifeng Wang
AU  - Kun Zhang
AU  - Zhuang Li
AU  - Hongxu Wang
PY  - 2016/05
DA  - 2016/05
TI  - Fuzzy Time Series Forecasting Model of Inverse Fuzzy Number Based on Percentage Year by Year of Continuous Point
BT  - Proceedings of the 2016 International Conference on Education, Management and Computer Science
PB  - Atlantis Press
SP  - 279
EP  - 284
SN  - 1951-6851
UR  - https://doi.org/10.2991/icemc-16.2016.58
DO  - https://doi.org/10.2991/icemc-16.2016.58
ID  - Wang2016/05
ER  -