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    基于深度学习的不完整时序数据补全方法综述

    Survey of Incomplete Time Series Imputation Methods Based on Deep Learning

    • 摘要: 时序数据广泛存在于工业、金融、交通、气象及医疗等领域,具有重要的分析应用价值. 然而由于人为或偶发因素导致的缺失,时序数据的完整性经常遭到破坏,进而削弱基于其开展的分析与决策的准确性和可靠性. 不完整时序数据通常包含复杂的时序依赖和变量间关系,为缺失值的有效补全带来较大挑战. 深度学习方法具有强大的建模能力,是应对该挑战的有效技术,并日益成为研究热点. 故系统综述基于深度学习的不完整时序数据补全方法的研究现状,首先介绍其相关概念和定义,进而对现有方法进行分类,归纳各类代表性方法的核心思想与特点. 随后整理常用的开源数据集与评价指标,并设计全面系统的实验方案. 实验从缺失场景、缺失率、变量数量、序列长度以及对下游任务的提升效果等多个角度,评估10种主流深度学习方法的补全质量与效率,最终总结实验结果并依据场景推荐合适的补全方法. 最后,结合当前研究进展对该领域的未来发展趋势进行展望,并总结全文.

       

      Abstract: Time series are widely present in domains such as industry, finance, transportation, meteorology, and healthcare, where they hold significant analytical and practical value. However, missing values caused by human errors or unforeseen events often compromise the completeness of time series, thereby undermining the accuracy and reliability of subsequent analysis and decision-making. Incomplete time series typically exhibit complex temporal dependencies and inter-variable correlations, making effective imputation a challenging task. Deep learning methods, with their powerful modeling capabilities, have emerged as a promising solution to address these challenges and have become an active area of research. We provide a comprehensive review of incomplete time series imputation methods based on deep learning. The survey begins by introducing the relevant concepts and definitions, followed by a systematic categorization and analysis of representative methods, highlighting their core ideas and characteristics. Commonly used open-source datasets and evaluation metrics are then summarized, along with a well-designed experimental framework. The experiments evaluate 10 mainstream deep learning models in terms of imputation performance and efficiency under various conditions, including different missing scenarios, missing rates, variable dimensions, sequence lengths, and downstream task performance. We analyze the experimental results to derive comprehensive insights, then recommend appropriate imputation methods based on these findings. Finally, we discuss current research trends and future directions in this field, concluding the survey.

       

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