Paper Title
Machine Learning Approaches For The Cosmetic Sales Forecasting

Abstract
In the contemporary information society, constructing an effective sales prediction model is challenging due to the sizeable amount of purchasing information obtained from diverse consumer preferences. Many empirical cases shown in the existing literature argue that the traditional forecasting methods, such as the index of smoothness, moving average, and time series, have lost their dominance of prediction accuracy when they are compared with the modern types of forecasting approaches, such as the neural network (NN) and support vector machine (SVM) models. To verify these findings, this paper utilizes the Taiwanese cosmetic sales data to examine three forecasting models, namely, the back propagation neural network (BPNN), least-square support vector machine (LSSVM), and auto regressive model (AR). The result concludes that the LS-SVM has the smallest mean absolute percent error (MAPE) and largest Pearson correlation coefficient (R2) between model and predicted values. Keywordsó Machine Learning, Back Propagation Neural Network, Least Square Support Vector Machine, Autoregressive Model, Model Performances Comparing.