Time Series Data Imputation: A Survey on Deep Learning Approaches

TL;DR

This survey reviews deep learning methods for time series data imputation, emphasizing how they leverage temporal relations to improve missing data recovery in various applications.

Contribution

It provides a comprehensive overview of deep learning architectures for time series imputation, highlighting their advantages, limitations, and recent progress in the field.

Findings

Deep learning models like RNN effectively capture temporal dependencies.

Recent methods improve imputation accuracy over traditional techniques.

The survey identifies key challenges and future directions in the field.

Abstract

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.