Predicting sales in a foodstore department using machine learning
Year: 2017
Type: dissertation
Abstract: Prediction of sales is an important field in the food industry and dueto new technologies it has recently gained alot of attention in order toimprove business operations and profitability. However, historicallythe industry has relied on traditional statistical models but in recentyears more advanced machine learning methods has gained traction.This study aims to compare three machine learning methods forsales prediction in the food industry: Multilayer Perceptron (MLP),Support Vector Machine (SVM) and Radial Basis Function Network(RBFN). The methods were compared in terms of their prediction accu-racy on daily sales in a food store department. The performance of themodels was determined using the performance measures: Mean Aver-age Percentage Error (MAPE) and Root Mean Squared Error (RMSE).The results show that the SVM performed lower error measuresthan the other two methods. The repeated measure analysis of vari-ance (rANOVA) was used in order to determine if there was a differ-ence between the methods. The test indicated a statistical significantdifference between the afformentioned methods.
Authors: Robert Siwerz, Christopher Dahlén
Language: English
Field: Decision Sciences
Domain: Social Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W2794197754