FINANCIAL TIME SERIES FORECASTING USING FUZZY REARRANGED INTERVALS
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초록
The fuzzy time series is introduced by Song and Chissom([8]) to con-struct a pattern for time series with vague or linguistic value. Many methods using the interval and fuzzy logical relationship related with historical data have been suggested to enhance the forecasting accuracy. But they do not fully re°ect the fluctuation of historical data. Therefore, we propose the interval rearranged method to reflect the fluctuation of historical data and to improve the forecasting accuracy of fuzzy time series. Using the well-known enrollment, the proposed method is dis-cussed and the forecasting accuracy is evaluated. Empirical studies show that the proposed method in forecasting accuracy is superior to existing methods and it fully re°ects the °uctuation of historical data.
Abstract
The fuzzy time series is introduced by Song and Chissom([8]) to con-struct a pattern for time series with vague or linguistic value. Many methods using the interval and fuzzy logical relationship related with historical data have been suggested to enhance the forecasting accuracy. But they do not fully re°ect the fluctuation of historical data. Therefore, we propose the interval rearranged method to reflect the fluctuation of historical data and to improve the forecasting accuracy of fuzzy time series. Using the well-known enrollment, the proposed method is dis-cussed and the forecasting accuracy is evaluated. Empirical studies show that the proposed method in forecasting accuracy is superior to existing methods and it fully re°ects the °uctuation of historical data.
참고문헌 (9)
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