Temporal Point Process

Temporal point processes (TPPs) model the timing and type of events occurring in continuous time, aiming to predict future events based on historical data. Current research heavily utilizes neural network architectures, particularly transformers and recurrent networks, often combined with techniques like diffusion models and contrastive learning, to improve prediction accuracy and efficiency, especially for long-horizon forecasting and high-dimensional data. This field is significant for its applications across diverse domains, including finance, healthcare, and social networks, enabling better understanding and prediction of complex event sequences. Recent work also emphasizes improving model interpretability and addressing challenges like computational cost and handling of irregularly spaced events.