Spatio Temporal Prediction

Spatio-temporal prediction focuses on forecasting future values of data exhibiting both spatial and temporal dependencies, aiming to improve accuracy and efficiency across diverse applications. Current research emphasizes developing robust models that generalize well to unseen data and handle data drift, focusing on architectures like Graph Neural Networks (GNNs), Multi-Layer Perceptrons (MLPs), and Long Short-Term Memory (LSTMs), often incorporating techniques like prompt tuning and contrastive learning. These advancements have significant implications for various fields, including urban planning (traffic prediction, resource allocation), environmental science (climate modeling), and transportation management, enabling more informed decision-making and improved resource optimization.