Comparison of Machine Learning Algorithms in Demand Prediction Problem

This study is intended to cover the major issues of applying econometric and machine learning techniques to a daily demand prediction problem. The current study is focused, mainly, on validating theoretical inferences about techniques advantages according to the empirical approach, therefore we pay special attention to the description and implementation of the methods. We include in the analysis the following prediction models: linear regression, support vector regression, random forest, gradient boosting, and ensemble from these models. At the same time we examine different accuracy metrics: quantile and mean absolute error. The purpose of the paper is going to be achieved via the models’ predictive power comparison on bakery retail chain data. Further research in this area of studies could push forward the deeper analysis of predictive techniques and retail daily sales compatibility.