High-Order Weighted Fuzzy Time Series Based on Different Discretization Approach

• Main Authors: Chung-Chi Liu, 劉仲琦
• Other Authors: Jing-Rong Chang
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/20646934805818173430

碩士 === 朝陽科技大學 === 資訊管理系碩士班 === 101 === There are many uncertainty problems in the Human society, such as the forecasting of economic growth rate, financial crisis, etc. Since Song and Chissom proposed the concept of fuzzy time series in 1993, many scholars have proposed different models to deal with these problems. However, previous studies usually did not consider the transfer original data to the fuzzy linguistic value by the subjective opinions in fuzzy process, which cannot objectively show the characteristics of the data. Based on above concepts, the purpose of this study is to explore ways of determining the objective lengths of intervals and amount of linguistic in fuzzy time series. This study proposed a high-order weighted fuzzy time series model based on variable length discretization approach (VLDA) and N-th quantile discretization approach (NQDA) to make forecasts. In order to verify the proposed method, the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) from the Taiwan Stock Exchange Corporation are used in the experiment, and the experiment results are compared with other methods in with this study. The forecasting performance shows that the proposed method having better forecasting ability. An intelligent decision support system (DSS) for stock market will be developed in this study. It is supposed to be a useful decision support tools for the investor to make better trading strategies in the future stock market.