This tutorial aims to provide signal processing (SP) and machine learning (ML) practitioners with vital tools, in an accessible way, to answer the question: How to deal with missing data? There are many strategies to handle incomplete signals. In this paper, we propose to group these strategies based on three common analytical tasks: i) missing-data imputation, ii) estimation with missing values and iii) prediction with missing values. We focus on methodological and experimental results through specific case studies on real-world applications. Promising and future research directions are also discussed. We hope that the proposed conceptual framework and the presentation of recent missing-data problems related will encourage researchers of the SP and ML communities to develop original methods and to efficiently deal with new applications involving missing data.

翻译：本教程旨在以易于理解的方式为信号处理（SP）和机器学习（ML）从业者提供关键工具，以回答以下问题：如何处理缺失数据？处理不完整信号存在多种策略。本文提出基于三种常见分析任务对这些策略进行分组：i）缺失数据插补，ii）含缺失值的估计，以及iii）含缺失值的预测。我们通过现实世界应用中的具体案例研究，重点关注方法论和实验结果。文中还讨论了具有前景的未来研究方向。我们希望所提出的概念框架以及对近期相关缺失数据问题的介绍，能够鼓励SP和ML领域的研究人员开发原创方法，并有效应对涉及缺失数据的新应用。