Enhancing Retail Sales Forecasting with LSTM Networks and Random Forest Regression: A Comparative Analysis

Abstract

This research paper presents a comprehensive comparative analysis of Long Short-Term Memory (LSTM) networks and Random Forest Regression (RFR) in forecasting retail sales, a critical function for optimizing inventory management and enhancing customer satisfaction. The study utilizes a robust dataset containing historical sales data from multiple retail stores, incorporating variables such as past sales figures, promotional events, and macroeconomic indicators. The LSTM model, a type of recurrent neural network designed to capture long-term dependencies, is employed to model the sequential nature of time-series sales data, while Random Forest Regression, an ensemble learning technique, is leveraged for its ability to handle non-linear relationships and interactions between variables. The performance of both models is evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The experimental results demonstrate that the LSTM network exhibits superior performance in capturing seasonality and trends within the data, achieving lower error rates compared to RFR. However, RFR provides more interpretability and robustness in scenarios with limited data. The findings suggest that while LSTM networks are advantageous for long-term forecasting, Random Forest Regression remains viable due to its scalability and ease of implementation. The paper concludes by discussing the implications for retail strategy, recommending a hybrid approach that combines the strengths of both models to optimize forecasting accuracy and operational efficiency in the retail sector.