Parameters Optimization Using Genetic Algorithms in Support Vector Regression for Sales Volume Forecasting

Abstract

Budgeting planning plays an important role in coordinating activities in organizations. An accurate sales volume forecasting is the key to the entire budgeting process. All of the other parts of the master budget are dependent on the sales volume forecasting in some way. If the sales volume forecasting is sloppily done, then the rest of the budgeting process is largely a waste of time. Therefore, the sales volume forecasting process is a critical one for most businesses, and also a difficult area of management. Most of researches and companies use the statistical methods, regression analysis, or sophisticated computer simulations to analyze the sales volume forecasting. Recently, various prediction Artificial Intelligent (AI) techniques have been proposed in forecasting. Support Vector Regression (SVR) has been applied successfully to solve problems in numerous fields and proved to be a better prediction model. However, the select of appropriate SVR parameters is difficult. Therefore, to improve the accuracy of SVR, a hybrid intelligent support system based on evolutionary computation to solve the difficulties involved with the parameters selection is presented in this research. Genetic Algorithms (GAs) are used to optimize free parameters of SVR. The experimental results indicate that GA-SVR can achieve better forecasting accuracy and performance than traditional SVR and artificial neural network (ANN) prediction models in sales volume forecasting.

Hybrid Corporate Performance Prediction Model Considering Technical Capability

“…Previous related studies have used only the financial information of a company to predict its performance or stock price [1][2][3][4][5][6][7]17]. However, a company's technical skills influence the performance and the stock price of a company as much as its financial status does.…”

“…Other studies search for the optimal parameter required for the SVR and SVM training [17,20,21]. Here, the most popular method is to use a genetic algorithm (GA), which mimics natural selection and the evolution process in nature [17,20,21].…”

“…Other studies search for the optimal parameter required for the SVR and SVM training [17,20,21]. Here, the most popular method is to use a genetic algorithm (GA), which mimics natural selection and the evolution process in nature [17,20,21]. Yuan (2012) suggested a hybrid prediction model using SVR and a GA to predict the sales volume of trucks and small automobiles of a Thailand based automobile company [17].…”

“…Other studies predict stock prices directly using support vector regression (SVR), which applies the SVM primarily as the classifier in the regression problem [3,17]. Hsu et al (2009) predicted the stock prices of companies using a self-organizing map (SOM) and SVR [3].…”

“…Here, the most popular method is to use a genetic algorithm (GA), which mimics natural selection and the evolution process in nature [17,20,21]. Yuan (2012) suggested a hybrid prediction model using SVR and a GA to predict the sales volume of trucks and small automobiles of a Thailand based automobile company [17]. The performance of the prediction model is highly influenced by the cost (C), gamma (σ), and epsilon (ε), which are the parameters used in the SVR training, and finding the optimal parameter values is not easy.…”