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PROJECT TITLE :

TAIEX Forecasting Using Fuzzy Time Series and Automatically Generated Weights of Multiple Factors

ABSTRACT:

In this paper, we gift a new method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) using fuzzy time series and automatically generated weights of multiple factors. The proposed technique uses the variation magnitudes of adjacent historical information to come up with fuzzy variation teams of the main factor (i.e., the TAIEX) and the elementary secondary factors (i.e., the Dow Jones, the NASDAQ and also the M1B), respectively. Based on the variation magnitudes of the main issue TAIEX and the elementary secondary factors of a specific trading day, it constructs the prevalence vector of the most factor and the incidence vectors of the elementary secondary factors on the trading day, respectively. By calculating the correlation coefficients between the numerical information series of the main issue and also the numerical data series of each elementary secondary factor, respectively, it calculates the relevance degree between the forecasted variation of the main issue and therefore the forecasted variation of every elementary secondary issue. Based on the correlation coefficients between the numerical knowledge series of the most issue and the numerical knowledge series of every elementary secondary issue on a trading day, it automatically generates the weights of the occurrence vector of the main factor and the prevalence vector of each elementary secondary factor on the trading day, respectively. Then, it calculates the forecasted variation of the most issue and also the forecasted variation of each elementary secondary factor on the trading day, respectively, to obtain the ultimate forecasted variation on the trading day. Finally, based on the closing index of the TAIEX on the trading day and the ultimate forecasted variation on the trading day, it generates the forecasted worth of the following trading day. The experimental results show that the proposed technique outperforms the existing strategies.


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