A Hybrid Method to Improve Forecasting Accuracy Utilizing Genetic Algorithm

F ocusing that the equation of exponential smoothing method(ESM) is equivalent to (1,1) order ARMA model equation, a new method of estimation of smoothing constant in exponential smoothing method is proposed before by us which satisfies minimum variance of forecasting error.Generally, smoothing constant is selected arbitrary.But in this paper, we utilize above stated theoretical solution.Firstly, we make estimation of ARMA model parameter and then estimate smoothing constants.Thus theoretical solution is derived in a simple way and it may be utilized in various fields.Furthermore, combining the trend removing method with this method, we aim to improve forecasting accuracy.An approach to this method is executed in the following method.T rend removing by the combination of linear and 2nd order non-linear function and 3rd order non-linear function is executed to the shipping data of food manufacture.Genetic Algorithm is utilized to search optimal weights for the weighting parameters of linear and non-linear function.F or the comparison, monthly trend is removed after that.T heoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non monthly trend removing data.Then forecasting is executed on these data.T he 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.