Estimation of Smoothing Constant of Minimum Variance Searching Optimal Parameters of Weight
Year: 2010
Type: article
Abstract: Focusing that the equation of exponential smoothing method(ESM) is equivalent to (1,1) order ARMA model equation, 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. 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. Trend removing by the combination of linear and 2nd order non-linear function and 3rd order non-linear function is executed to the original shipping data of consumer goods. The weights for these functions are set 0.5 for two patterns at first and then varied by 0.01 increment for three patterns and optimal weights are searched. For the comparison, monthly trend is removed after that. Theoretical 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. Comparison between this method and non trend removing method is executed. Good result is obtained.
Authors: Keiko Nagata, Kazuhiro Takeyasu
Institution: Osaka Prefecture University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W1577569604