Harnessing LSTM Neural Networks and Hyperparameter Optimization for Precise Sales Forecasting in Retail

Abstract

In the dynamic and competitive retail industry, precise sales forecasting is pivotal for operational efficiency and strategic planning. This study introduces an advanced forecasting model that leverages Long Short-Term Memory (LSTM) neural networks, enriched with hyperparameter optimization through Keras Tuner, to predict daily sales in the challenging environment of Walmart’s retail dataset. Our dataset encompasses hierarchical sales data across three pivotal US states—California, Texas, and Wisconsin—covering a wide spectrum of product categories and store details, including influential factors such as price variations, promotions, and special events. We meticulously crafted an LSTM-based model, calibrated through extensive hyperparameter tuning, to capture the intricate patterns and temporal dependencies inherent in the sales data. The research methodology adopted a systematic approach, splitting the dataset into training and testing subsets, followed by rigorous evaluation of model performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Our findings demonstrate a significant enhancement in forecast accuracy compared to traditional forecasting methods, underpinning the effectiveness of LSTM networks in handling complex sales forecasting tasks. This paper contributes to the body of knowledge by providing insights into applying deep learning and hyperparameter optimization techniques in retail sales forecasting. It offers a robust model for retailers to refine their forecast accuracy and decision-making processes.