Deep Time-Series Forecasting in 10 Years: A Survey
The survey reviews deep time-series forecasting by focusing on autocorrelation, which is data's dependence on past values. It categorizes research into two areas: neural architectures designed to handle input history patterns and learning objectives used to model output label sequences. This provides a unified framework for understanding how deep learning models capture temporal dependencies.