Sales Prediction Based on Machine Learning Approach

To help brick-and-mortar merchants set reasonable sales goals, this study proposes and implements a retail sales forecast method based on machine learning theory. Specifically, this paper used the XGBoost model, LightGBM tree structure model, long and short-term memory network (LSTM) model and model fusion method, took the sales data of 1115 physical stores of Rossmann of Germany as the research object, used three single models and three fusion models to predict sales. First, the three single models were trained and verified through feature engineering and parameter tuning; then the three single models were fused via three weighted average methods with different weights, and the fusion model was optimized and verified. Finally, two evaluation indexes, MAPE and RMSPE, were implemented to evaluate the model, and the MAPE and RMSPE values of several models were compared. Experimental results indicated that the MAPE and RMSPE values of the single model were above 0.049 and 0.065, respectively, while the MAPE and RMSPE values of the fusion model were below 0.047 and 0.062, respectively. It showed that although the single model method was effective and feasible, the fusion method effectively improved the prediction accuracy and generalization ability of the model, and obtained better performance than the single model.