Event Forecasting

Event forecasting aims to predict future occurrences, leveraging historical data and various contextual factors to improve accuracy and reliability. Current research heavily utilizes large language models (LLMs) and neural networks, including recurrent neural networks (RNNs), graph neural networks (GNNs), and transformers, often incorporating multimodal data (text, images) and advanced techniques like contrastive learning and retrieval-augmented generation. This field is significant for its potential applications in diverse areas such as public safety, resource allocation, and strategic decision-making, with ongoing efforts focused on improving model robustness, handling temporal complexities, and addressing issues like data bias and long-tail effects.