Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
978729 | Physica A: Statistical Mechanics and its Applications | 2006 | 11 Pages |
Abstract
Fuzzy time series models have been applied to handle nonlinear problems. To forecast fuzzy time series, this study applies a backpropagation neural network because of its nonlinear structures. We propose two models: a basic model using a neural network approach to forecast all of the observations, and a hybrid model consisting of a neural network approach to forecast the known patterns as well as a simple method to forecast the unknown patterns. The stock index in Taiwan for the years 1991–2003 is chosen as the forecasting target. The empirical results show that the hybrid model outperforms both the basic and a conventional fuzzy time series models.
Related Topics
Physical Sciences and Engineering
Mathematics
Mathematical Physics
Authors
Kunhuang Huarng, Tiffany Hui-Kuang Yu,