深度时间序列预测十年综述：从自相关建模视角统一骨干架构与损失函数

Authors:Hao Wang, Licheng Pan, Qingsong Wen, Jialin Yu, Zhichao Chen, Chunyuan Zheng, Xiaoxi Li, Zhixuan Chu, Chao Xu, Mingming Gong, Haoxuan Li, Yuan Lu, Zhouchen Lin, Philip Torr, Yan Liu

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Abstract: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 architectures and loss functions, whereas prior surveys provide limited coverage of the latter. Second, it analyzes the motivations and insights underlying the surveyed literature from a unified autocorrelation perspective, providing a holistic overview of the field's evolution. Additional resources and details are available at this https URL.

Comments:	This survey is accepted by IEEE TPAMI
Subjects:	Machine Learning (stat.ML); Machine Learning (cs.LG); Applications (stat.AP)
Cite as:	arXiv:2603.19899 [stat.ML]
	(or arXiv:2603.19899v2 [stat.ML] for this version)
	https://doi.org/10.48550/arXiv.2603.19899 arXiv-issued DOI via DataCite
Journal reference:	IEEE Transactions on Pattern Analysis and Machine Intelligence, 2026

Submission history

From: Hao Wang [view email]
[v1] Fri, 20 Mar 2026 12:31:08 UTC (13,142 KB)
[v2] Tue, 6 Oct 2026 11:43:59 UTC (10,263 KB)