A Hybrid Method to Improve Forecasting Accuracy in the Case of Sanitary Materials Data

Abstract

Sales forecasting is a starting point of supply chain management, and its accuracy influences business management significantly. In industries, how to improve forecasting accuracy such as sales, shipping is an important issue. In this paper, a hybrid method is introduced and plural methods are compared. Focusing 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 Takeyasu et.al. which satisfies minimum variance of forecasting error. Firstly, we make estimation of ARMA model parameter and then estimate smoothing constants. In this paper, combining the trend removing method with this method, we aim to improve forecasting accuracy. Trend removing by the combination of linear and 2nd order non-linear function and 3rd order nonlinear function is carried out to the manufacturer's data of sanitary materials.The new method shows that it is useful for the time series that has various trend characteristics and has rather strong seasonal trend. The effectiveness of this method should be examined in various cases.

A Hybrid Method to Improve Forecasting Accuracy Utilizing Genetic Algorithm –An Application to the Data of Operating equipment and supplies

“…[2] Non-linear function We set (19) ( 20) as a 2nd and a 3rd order non-linear function. and are also parameters for a 2nd and a 3rd order non-linear functions which are estimated by using least square method.…”