Fusion-model Based Fuzzy Time-series for Stock Market Forecasting

• Main Authors: Tai-Liang Chen, 陳泰良
• Other Authors: Ching-Hsue Cheng
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/00952174864322552333

博士 === 雲林科技大學 === 管理研究所博士班 === 96 === Stock investors usually make their short-term investment decisions according to recent stock information such as the news before market opened, the late technical analysis reports, and the price fluctuations in these two days. To reflect these practical price fluctuations or patterns caused by complex stock market variables, this dissertation provides a comprehensive fuzzy time-series, fusion-model based fuzzy time-series, which factors two relationships, contained in time-series, into forecasting processes: (1) linear relationships between recent periods of stock prices, and (2) fuzzy logical relationships (non-linear relationships) mined from time-series. Additionally, to improve the forecasting performance of past fuzzy time-series models, two weighted methods (trend weighted and frequency weighted) and defuzzification methods (spread-center and gravity-center) are employed in forecasting process and, therefore, four types of fusion models are issued.