Unified Prediction

Unified prediction aims to develop single models capable of accurately forecasting diverse types of data and scenarios, avoiding the need for separate models tailored to specific tasks. Current research focuses on adapting and extending transformer architectures, particularly generative models, to handle various data modalities (e.g., time series, images, and tabular data) and incorporate contextual information. This approach promises improved efficiency and generalization across applications, ranging from autonomous driving and resource management to educational outcomes and cellular network planning, by leveraging shared underlying patterns and reducing the need for extensive, task-specific training data.