Year: 2025
Type: article
Abstract: In an era defined by data-driven decision-making, accurate sales trend prediction has become a strategic imperative for organizations seeking competitive advantage. This research explores the integration of advanced data analytics techniques—specifically ARIMA, LSTM, and XGBoost—for forecasting sales trends using both synthetic and real-world datasets. The study critically examines the limitations of traditional statistical models in handling nonlinear, seasonal, and high-frequency data and highlights the superiority of machine learning and deep learning approaches in capturing complex patterns. Through rigorous experimentation and model evaluation using performance metrics such as MAE, MSE, and RMSE, this research demonstrates the effectiveness of intelligent forecasting systems. Furthermore, the paper reviews the theoretical underpinnings, recent advancements, and real-world applications of these models while addressing the challenges of data quality, model interpretability, and real-time deployment. The findings underscore the potential of predictive analytics to transform business forecasting, improve operational efficiency, and drive data-informed strategies.
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4409802031