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 -- whereas prior surveys neglect or inadequately discuss the latter aspect. Second, it offers a thorough analysis of the motivations, insights, and progression of the surveyed literature from a unified, autocorrelation-centric perspective, providing a holistic overview of the evolution of deep time-series forecasting. The full list of papers and resources is available at https://github.com/Master-PLC/Awesome-TSF-Papers.

翻译：自相关是时间序列数据的关键特征，表现为每个观测值与其历史值之间存在统计依赖关系。在深度时间序列预测的背景下，自相关同时存在于输入历史序列与标签序列中，由此衍生出两个核心研究问题：(1) 设计能对历史序列自相关进行建模的神经网络架构；(2) 构建能对标签序列自相关进行建模的学习目标。近期研究虽已在此类问题中取得进展，但尚缺乏对这两个维度开展系统性综述的研究。为弥补这一空白，本文从自相关建模视角对深度时间序列预测进行了全面综述。与现有综述相比，本文具有两项独特贡献：第一，提出了一种涵盖近期模型架构与学习目标研究的新分类体系——而现有综述往往忽视或未充分讨论后者；第二，从统一的自相关中心视角出发，深入分析了现有文献的动机、洞见与演进脉络，为深度时间序列预测的发展历程提供了全局性概述。完整论文清单与资源请见 https://github.com/Master-PLC/Awesome-TSF-Papers。