Universal Time-Series Representation Learning: A Survey

Time-series data exists in every corner of real-world systems and services, ranging from satellites in the sky to wearable devices on human bodies. Learning representations by extracting and inferring valuable information from these time series is crucial for understanding the complex dynamics of particular phenomena and enabling informed decisions. With the learned representations, we can perform numerous downstream analyses more effectively. Among several approaches, deep learning has demonstrated remarkable performance in extracting hidden patterns and features from time-series data without manual feature engineering. This survey first presents a novel taxonomy based on three fundamental elements in…

• MO
  Ministry of Science and ICT, South Korea
• IF
  Institute for Information and Communications Technology Promotion

  Awards: 2020-0-00862, 2022-0-00157, 2022-