Journal
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
Volume 363, Issue 2, Pages 481-491Publisher
ELSEVIER
DOI: 10.1016/j.physa.2005.08.014
Keywords
backpropagation; forecasting; nonlinear; stock index
Categories
Ask authors/readers for more resources
Fuzzy time series models have been applied to handle nonlinear problems. To forecast fuzzy time series, this study applies a back propagation 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. (c) 2005 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available