A Deep Learning Approach for the Prediction of Retail Store Sales

Abstract

The purpose of this research is to construct a sales prediction model for retail stores using the deep learning approach, which has gained significant attention in the rapidly developing field of machine learning in recent years. Using such a model for analysis, an approach to store management could be formulated. The present study uses three years' worth of point-of-sale (POS) data from a retail store to construct a sales prediction model that, given the sales of a particular day, predicts the changes in sales on the following day. As a result, a deep learning model that considers the L1 regularization achieved a sale forecasting accuracy rate of 86%. The products at the retail store have been finely categorized. Even if the attributes of the product categories are increased in number from tens to thousands, the predictive accuracy did not fall by more than about 7%. In contrast, the accuracy decreased by around 13% when the logistic regression model was used. These results indicate that deep learning is highly suitable for constructing models that include multi-attribute variables. The present research demonstrates that deep learning is effective for analyzing the POS data of retail stores.

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