Spatio Temporal Point Process

Spatio-temporal point processes (STPPs) are statistical models used to analyze and predict events occurring at specific locations and times. Current research focuses on developing more accurate and flexible STPP models, often employing neural networks (like recurrent or transformer architectures) to learn complex spatio-temporal dependencies and handle high-dimensional data, addressing challenges like intractable likelihood calculations and the need for uncertainty quantification in predictions. These advancements are significantly impacting diverse fields, improving forecasting accuracy in areas such as epidemiology, seismology, and traffic flow prediction, and enabling more sophisticated analysis of complex event data.