Deep Time-Series Forecasting in 10 Years: A Survey
Autocorrelation is a common property of time-series, where each observation is dependent on its predecessors. In deep time-series forecasting, it raises two central challenges: (1) designing backbone architectures to model autocorrelation in history sequences, and (2) devising loss functions to model autocorrelation in label sequences. Recent studies have made strides in tackling these challenges, but a systematic survey examining both aspects remains lacking. To bridge this gap, this paper reviews deep time-series forecasting from an autocorrelation modeling perspective, offering two contributions beyond existing surveys. First, it introduces a taxonomy that jointly covers both backbone ...
| Authors | Hao Wang, Licheng Pan, Qingsong Wen et al. |
|---|---|
| Primary category | stat.ML |
| Published | 20 March 2026 |
| Last updated at source | 6 October 2026 |
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