Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review

Spatio-temporal data proliferates in numerous real-world domains, such as transportation, weather, and energy. Spatio-temporal deep learning models aims to utilize useful patterns in such data to support tasks like prediction, imputation, and anomaly detection. However, previous one-to-one deep learning models designed for specific tasks typically require separate training for each use case, leading to increased computational and storage costs. To address this issue, one-to-many spatio-temporal foundation models have emerged, offering a unified framework capable of solving multiple spatio-temporal tasks. These foundation models achieve remarkable success by learning general knowledge with spatio-temporal data…

• NN
  National Natural Science Foundation of China

  Award: 62472068