A Multivariate Heuristic Model for Fuzzy Time-Series Forecasting

Fuzzy time-series models have been widely applied due to their ability to handle nonlinear data directly and because no rigid assumptions for the data are needed. In addition, many such models have been shown to provide better forecasting results than their conventional counterparts. However, since...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2007-08, Vol.37 (4), p.836-846
Main Authors: Huarng, Kun-Huang, Yu, Tiffany Hui-Kuang, Hsu, Yu Wei
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computational complexity
Computer Simulation
Councils
Cybernetics
Data processing
Decision Support Techniques
Financial data processing
Forecasting
Fuzzy
Fuzzy Logic
Fuzzy set theory
Fuzzy sets
Fuzzy systems
Heuristic
International trade
Mathematical analysis
Mathematical models
Models, Statistical
Multivariate Analysis
Pattern Recognition, Automated - methods
Predictive models
Public finance
Temperature
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Summary:Fuzzy time-series models have been widely applied due to their ability to handle nonlinear data directly and because no rigid assumptions for the data are needed. In addition, many such models have been shown to provide better forecasting results than their conventional counterparts. However, since most of these models require complicated matrix computations, this paper proposes the adoption of a multivariate heuristic function that can be integrated with univariate fuzzy time-series models into multivariate models. Such a multivariate heuristic function can easily be extended and integrated with various univariate models. Furthermore, the integrated model can handle multiple variables to improve forecasting results and, at the same time, avoid complicated computations due to the inclusion of multiple variables.
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/TSMCB.2006.890303