Enhancing Demand Forecasting Performance Using Deep Learning and Time Series Data Augmentation Techniques - Jinseop Yun (International Journal of Intelligent Systems and Applications in Engineering, 2025)
Accurately forecasting demand remains a persistent challenge for organizations, especially under conditions of high uncertainty and data scarcity. While machine learning and deep learning methods have advanced beyond traditional statistical approaches, their effectiveness is often constrained by limited data availability. To address this critical issue, this paper introduces an innovative and systematic framework that integrates advanced time series data augmentation techniques, the Long Short-Term Memory (LSTM) deep learning model, and Average Demand Interval-Coefficient of Variance (ADI-CV) methodology. The proposed framework leverages ADI-CV to categorize time series patterns, enabling the application of tailored augmentation techniques such as Moving Block Bootstrap (MBB), Time-Conditional GAN (T-CGAN), and Transformer-based Time-Series Conditional GAN (TTS-CGAN). These techniques ensure the generation of synthetic data that accurately reflects temporal characteristics and market conditions, overcoming the traditional limitations of data scarcity. Our experimental results demonstrate that the augmented time series data significantly enhances forecasting performance across diverse and complex demand scenarios. This framework not only addresses the critical gap in demand forecasting methodologies but also establishes a scalable and adaptable solution for enterprises operating in volatile and dynamic market environments. By offering a robust tool to improve predictive accuracy and reliability, this study contributes a novel methodology with the potential to transform business decision-making processes.