  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.
