论文标题
时间序列数据插补：关于深度学习方法的调查
Time Series Data Imputation: A Survey on Deep Learning Approaches
论文作者
论文摘要
时间序列在现实世界中都存在。但是，意外的事故（例如传感器破裂或信号丢失）会导致时间序列中缺少值，从而难以利用数据。然后，它会损害下游应用程序，例如传统分类或回归，顺序数据集成和预测任务，从而提高了对数据插补的需求。当前，插入时间序列数据是一个有很多方法的问题，方法是不同类别的方法。但是，这些作品很少在观测值之间使用时间关系，而将时间序列视为正常的结构化数据，从而丢失了时间数据中的信息。在最近，深度学习模型引起了极大的关注。基于深度学习的时间序列方法已通过RNN等模型的使用取得了进步，因为它从数据中捕获了时间信息。在本文中，我们主要关注使用深度学习方法的时间序列插补技术，这最近在该领域取得了进步。我们将审查和讨论他们的模型架构，他们的利弊以及它们的效果，以展示时间序列插补方法的发展。
Time series are all around in real-world applications. However, unexpected accidents for example broken sensors or missing of the signals will cause missing values in time series, making the data hard to be utilized. It then does harm to the downstream applications such as traditional classification or regression, sequential data integration and forecasting tasks, thus raising the demand for data imputation. Currently, time series data imputation is a well-studied problem with different categories of methods. However, these works rarely take the temporal relations among the observations and treat the time series as normal structured data, losing the information from the time data. In recent, deep learning models have raised great attention. Time series methods based on deep learning have made progress with the usage of models like RNN, since it captures time information from data. In this paper, we mainly focus on time series imputation technique with deep learning methods, which recently made progress in this field. We will review and discuss their model architectures, their pros and cons as well as their effects to show the development of the time series imputation methods.
