title: A multivariate heuristic model for fuzzy time-series forecasting.
abstract: 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.
authors: Department of International Trade, Feng Chia University, Taichung 40724, Taiwan, ROC. khhuarng@fcu.edu.tw
authors: Kun-Huang
authors: KH
authors: Huarng
authors: Tiffany Hui-Kuang
authors: TH
authors: Yu
authors: Yu Wei
authors: YW
authors: Hsu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003661:Decision Support Techniques; D005544:Forecasting; D017143:Fuzzy Logic; D015233:Models, Statistical; D015999:Multivariate Analysis; D010363:Pattern Recognition, Automated / Q000379:methods*