Forecasting Architecture

Forecasting architecture focuses on designing and optimizing models to accurately predict future outcomes from time series data, particularly in complex systems with spatial and temporal dependencies. Current research emphasizes hybrid architectures combining explicit domain knowledge with learned relational structures, leveraging techniques like graph neural networks, hypergraph learning, and memory-augmented approaches to improve forecasting accuracy and handle non-stationary data. These advancements are crucial for applications ranging from digital twin technology and autonomous vehicles to resource management and environmental monitoring, enabling more informed decision-making and proactive interventions.