AI-DRIVEN SALES FORECASTING USING LONG SHORT-TERM MEMORY (LSTM) NETWORKS

Abstract

Sales forecasting is a critical component of business strategy, enabling organizations to make informed decisions regarding inventory management, production planning, and marketing optimization. Traditional statistical forecasting methods, such as ARIMA and exponential smoothing, often struggle to model nonlinear and complex temporal dependencies present in real-world sales data. To address these limitations, this study proposes an AI-driven sales forecasting model using Long Short-Term Memory (LSTM) networks, a specialized type of recurrent neural network (RNN) capable of capturing long-term dependencies in sequential data. The proposed LSTM-based framework processes historical sales data along with influencing factors such as seasonal trends, pricing, promotions, and holidays to predict future sales with high precision. The model incorporates data preprocessing, feature normalization, and hyperparameter tuning to enhance learning efficiency and generalization. Experimental results demonstrate that the LSTM model outperforms traditional machine learning techniques like Linear Regression, Random Forest, and ARIMA, achieving lower prediction errors and higher correlation with actual sales trends. This AI-driven approach provides an adaptive, data-driven, and highly accurate forecasting solution, enabling businesses to improve demand planning, minimize overstock and stockout risks, and enhance overall operational efficiency. The results validate the effectiveness of LSTM networks as a powerful tool for intelligent, real-time sales forecasting in dynamic business environments.