Demand Prediction

Demand prediction aims to accurately forecast future demand for various products or services, informing crucial operational and strategic decisions across diverse sectors. Current research emphasizes the development of sophisticated models, leveraging architectures like graph neural networks, transformers, and hybrid approaches combining deep learning with traditional statistical methods, to capture complex spatiotemporal dependencies and incorporate external factors like weather or socio-economic data. These advancements are improving prediction accuracy and enabling more efficient resource allocation in areas such as urban transportation, supply chain management, and e-commerce, while also addressing challenges like data privacy and fairness. The field is increasingly focused on developing interpretable models and addressing biases to enhance both accuracy and equitable outcomes.