Asynchronous time series are often observed in several applications such as health care, astronomy, and climate science, and pose a significant challenge to the standard deep learning architectures.
Because of the asynchronous nature, they pose a significant challenge to deep learning architectures, which presume that the time series presented to them are regularly sampled, fully observed, and aligned with respect to time.
Learning complex time series forecasting models usually requires a large amount of data, as each model is trained from scratch for each task/data set.
Time series data is ubiquitous in research as well as in a wide variety of industrial applications.
Ranked #2 on Time Series Forecasting on ETTh2 (720)