期刊
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
卷 363, 期 2, 页码 481-491出版社
ELSEVIER
DOI: 10.1016/j.physa.2005.08.014
关键词
backpropagation; forecasting; nonlinear; stock index
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据