Enhancing Retail Sales Forecasting through LSTM Networks and ARIMA Models: A Comparative Analysis of AI Methodologies

Abstract

This research paper presents a comprehensive analysis of advanced methodologies for enhancing retail sales forecasting, focusing on the use of Long Short-Term Memory (LSTM) networks and Autoregressive Integrated Moving Average (ARIMA) models. The study investigates the efficacy of these approaches in capturing the complex temporal patterns intrinsic to retail sales data, characterized by seasonality, trends, and cyclical fluctuations. By deploying a comparative framework, the research evaluates the performance of LSTM, a notable deep learning architecture designed to handle sequence prediction problems, against the traditional ARIMA model, renowned for its statistical robustness in time series forecasting. The analysis encompasses a diverse dataset from multiple retail sectors, allowing for a nuanced exploration of model adaptability and accuracy across varied market conditions. Key performance metrics, including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), serve as benchmarks for model comparison. Results indicate that while ARIMA provides reliable forecasts in scenarios with linear patterns and short-term dependencies, LSTMs demonstrate superior performance in capturing long-term dependencies and nonlinear relationships, significantly enhancing forecast accuracy. The paper concludes with insights into the strategic integration of LSTM networks in retail operations, proposing a hybrid model approach to leverage the strengths of both methodologies for optimal forecasting outcomes. This study contributes to the field of retail analytics by offering a detailed evaluation of contemporary AI-driven forecasting techniques, providing practitioners with informed guidance for improving data-driven decision-making processes.