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A Hybrid Method to Improve Forecasting Accuracy Utilizing Genetic Algorithm and Its Application to Stock Market Price Data
*Yasuo IshiiKazuhiro Takeyasu
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Focusing that the equation of the exponential smoothing method (ESM) is equivalent to (1,1) order ARMA model equation, a new method of estimation of the smoothing constant in the exponential smoothing method was proposed before by us which satisfied the minimum variance of forecasting error. In this paper, we utilize the above stated theoretical solution. Firstly, we estimated the ARMA model parameter and then estimate the smoothing constants. Thus the theoretical solution is derived in a simple way and it may be utilized in various fields. This new method shows that it is useful for the time series that has various trend characteristics. The effectiveness of this method should be examined in various cases. Keywords: minimum variance, exponential smoothing method, forecasting, trend, genetic algorithm
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