arXiv 2402.040592025-05-20cs.LGcs.AI

面向多元时间序列插补的深度学习：综述

Deep Learning for Multivariate Time Series Imputation: A Survey

PyPOTS Research（PyPOTS研究机构）；Hong Kong University of Science and Technology（香港理工大学）；University of Oxford（牛津大学）；King’s College London（伦敦大学学院）；Huawei Noah’s Ark Lab（华为诺亚实验室）；Hong Kong University of Science and Technology (Guangzhou)（香港理工大学（广州））；Squirrel Ai Learning（Squirrel Ai学习机构）

Jun Wang, Wenjie Du, Yiyuan Yang, Linglong Qian, Wei Cao, Keli Zhang, Wenjia Wang, Yuxuan Liang, Qingsong Wen

备注：: Accepted by IJCAI 2025

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AI总结：

该综述全面梳理多元时间序列插补的深度学习方法，提出基于插补不确定性和神经网络架构的新分类体系，介绍PyPOTS等工具包，探讨挑战与未来方向，为相关研究和实践提供参考。

摘要：

缺失值在多元时间序列（MTS）数据中普遍存在，给精准分析及下游应用带来了显著挑战。近年来，基于深度学习的方法通过利用复杂的时间依赖关系与学习到的数据分布，已能成功处理缺失数据问题。本综述对多元时间序列插补（MTSI）任务中的深度学习方法进行了全面梳理。我们提出了一种全新的分类体系，从插补不确定性与神经网络架构两个核心视角对现有方法进行归类。此外，我们总结了现有的MTSI工具包，重点介绍了PyPOTS生态系统，该生态系统为MTSI研究提供了集成化、标准化的基础。最后，我们探讨了关键挑战与未来研究方向，为后续MTSI研究提供思路。本综述旨在为时间序列分析与缺失数据插补任务领域的研究者和从业者提供有价值的参考。一份维护良好的MTSI论文与工具清单可在https://github.com/WenjieDu/Awesome_Imputation获取。

英文摘要：

Missing values are ubiquitous in multivariate time series (MTS) data, posing significant challenges for accurate analysis and downstream applications. In recent years, deep learning-based methods have successfully handled missing data by leveraging complex temporal dependencies and learned data distributions. In this survey, we provide a comprehensive summary of deep learning approaches for multivariate time series imputation (MTSI) tasks. We propose a novel taxonomy that categorizes existing methods based on two key perspectives: imputation uncertainty and neural network architecture. Furthermore, we summarize existing MTSI toolkits with a particular emphasis on the PyPOTS Ecosystem, which provides an integrated and standardized foundation for MTSI research. Finally, we discuss key challenges and future research directions, which give insight for further MTSI research. This survey aims to serve as a valuable resource for researchers and practitioners in the field of time series analysis and missing data imputation tasks.A well-maintained MTSI paper and tool list are available at https://github.com/WenjieDu/Awesome_Imputation.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。