Demand Forecasting

Demand forecasting aims to accurately predict future demand for products or services, crucial for optimizing inventory, pricing, and resource allocation across various sectors. Current research emphasizes improving forecasting accuracy, particularly during peak periods or under anomalous conditions, using advanced machine learning models such as transformers, graph neural networks (GNNs), and hybrid architectures combining convolutional neural networks (CNNs) with recurrent neural networks (RNNs like LSTM and GRU). These models often incorporate multimodal data (e.g., images, text, macroeconomic indicators) and leverage external knowledge sources (e.g., world events) to enhance predictive power and address challenges like data sparsity and cold-start problems. The resulting improvements in forecasting accuracy have significant implications for supply chain management, e-commerce, energy grids, and other industries reliant on accurate demand predictions.