Neural Network Approaches to Temporal Pattern Recognition: Applications in Demand Forecasting and Predictive Analytics

Abstract

Temporal pattern recognition has become increasingly critical for predictive analytics in various domains, particularly in demand forecasting where accurate predictions directly impact business operations and profitability. Neural network (NN) architectures have demonstrated remarkable capabilities in capturing complex temporal dependencies within sequential data, outperforming traditional statistical methods in numerous applications. This review examines the evolution and application of neural network approaches specifically designed for temporal pattern recognition, with emphasis on their utilization in demand forecasting and predictive analytics. The paper provides a comprehensive analysis of recurrent neural networks (RNNs), long short-term memory (LSTM) networks, gated recurrent units (GRUs), convolutional neural networks (CNNs), and transformer-based architectures in the context of time series forecasting. Furthermore, this review explores the integration of attention mechanisms, the emergence of spatiotemporal graph neural networks (STGNNs), and hybrid model architectures that combine multiple approaches to enhance forecasting accuracy. The evaluation metrics commonly employed to assess model performance, including mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE), are discussed alongside benchmark datasets utilized in the field. Through systematic examination of recent literature spanning from 2019 to 2025, this review identifies key architectural innovations, practical applications in retail and supply chain management, and emerging trends that define the current state of temporal pattern recognition. The findings reveal that while transformer-based models have gained significant attention for long-sequence forecasting, simpler linear architectures and hybrid approaches often demonstrate competitive or superior performance depending on dataset characteristics and application requirements. This comprehensive review serves as a foundation for researchers and practitioners seeking to understand the landscape of neural network methodologies for temporal pattern recognition and their practical deployment in demand forecasting systems.