[2603.19899] Deep Autocorrelation Modeling for Time-Series Forecasting: Progress and Prospects

Statistics > Machine Learning arXiv:2603.19899 (stat) [Submitted on 20 Mar 2026] Title:Deep Autocorrelation Modeling for Time-Series Forecasting: Progress and Prospects 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 View a PDF of the paper titled Deep Autocorrelation Modeling for Time-Series Forecasting: Progress and Prospects, by Hao Wang and 14 other authors View PDF HTML (experimental) Abstract:Autocorrelation is a defining characteristic of time-series data, where each observation is statistically dependent on its predecessors. In the context of deep time-series forecasting, autocorrelation arises in both the input history and the label sequences, presenting two central research challenges: (1) designing neural architectures that model autocorrelation in history sequences, and (2) devising learning objectives that 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 provides a comprehensive review of deep time-series forecasting from the perspective of autocorrelation modeling. In contrast to existing surveys, this work makes two distinctive contributions. First, it proposes a novel taxonomy that encompasses recent literature on both model architectures and learning objectives -- wher...