Optimizing Sales Forecasting: Implementing a Deep Learning Model for Accurate and Timely Predictions

The ever-changing customer demands present a complex challenge for retail stores that strive to satisfy their customers while maximizing their profits. To achieve this, retail stores typically use sales forecasting techniques. However, due to the limitations of traditional methods, many retail stores are unable to make informed business decisions because they lack sufficient forecast information. To address this issue, this research proposes the use of a deep learning model optimized by a genetic algorithm to predict future sales. To evaluate the effectiveness of this approach, the authors conducted a comparative evaluation between the proposed deep learning model optimized by a genetic algorithm and the auto ARIMA model. The results of the study demonstrate the superiority of the proposed approach in providing accurate sales forecasts. The documented findings can serve as a valuable resource for retail stores seeking to improve their sales forecasting capabilities.