Comparison of Deep Learning Algorithms for Retail Sales Forecasting

We investigate the use of deep learning models for retail sales predictions in this research. Having a proper sales forecasting can lead to optimization in inventory management, marketing strategies, and other core business operations. This research proposed to assess deep learning models such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Multilayer Perceptron and hybrid CNN-LSTM model. The models are further improved by using some dense layers to embed daily sales data from the biggest pharmaceutical firm in the study. Models are then trained on 80% of the dataset and tested on remaining 20%. The accuracy of the proposed research is compared using evaluation metrics like Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results indicate that the CNN-LSTM model outperforms the others, achieving the lowest RMSE and MAE values, making it the most suitable for sales forecasting in the given context. This research contributes to the field by demonstrating the superiority of hybrid models in handling complex, temporal data for predictive analytics. Future work will explore the integration of additional data sources and advanced deep learning architectures to further improve forecasting accuracy and applicability.