[论文解读] Generative adversarial networks in time series: A survey and taxonomy

Eoin Brophy, Zhengwei Wang+2 more|arXiv (Cornell University)|Jul 23, 2021

Generative Adversarial Networks and Image Synthesis参考文献 96被引用 46

一句话总结

本论文综述了时间序列的GAN变体，提出一个将离散变体和连续变体GAN分开的分类法，并讨论架构、评估指标和隐私考量。

ABSTRACT

Generative adversarial networks (GANs) studies have grown exponentially in the past few years. Their impact has been seen mainly in the computer vision field with realistic image and video manipulation, especially generation, making significant advancements. While these computer vision advances have garnered much attention, GAN applications have diversified across disciplines such as time series and sequence generation. As a relatively new niche for GANs, fieldwork is ongoing to develop high quality, diverse and private time series data. In this paper, we review GAN variants designed for time series related applications. We propose a taxonomy of discrete-variant GANs and continuous-variant GANs, in which GANs deal with discrete time series and continuous time series data. Here we showcase the latest and most popular literature in this field; their architectures, results, and applications. We also provide a list of the most popular evaluation metrics and their suitability across applications. Also presented is a discussion of privacy measures for these GANs and further protections and directions for dealing with sensitive data. We aim to frame clearly and concisely the latest and state-of-the-art research in this area and their applications to real-world technologies.

研究动机与目标

• 全面回顾应用于时间序列生成与预测的GAN。
• 提出区分离散变体和连续变体时间序列GAN的分类法。
• 总结该领域的架构、损失函数、评估指标、数据集以及隐私方面的考量。

提出的方法

• 提出将时间序列GAN分成离散变体与连续变体两类的分类法。
• 总结并比较具有代表性的模型（例如SeqGAN、C-RNN-GAN、RCGAN、TimeGAN、SigCWGAN）及其架构。
• 讨论训练中的挑战（稳定性、梯度消失、模式崩溃）、评估方法与隐私风险。
• 回顾在时间序列GAN研究中使用的流行数据集以及缺乏标准基准数据集的问题。
• 描述与合成时间序列数据相关的隐私保护方面与监管考量。

实验结果

研究问题

• RQ1 时间序列GAN的主要类型有哪些（离散变体与连续变体）及其特征架构？
• RQ2 时间序列GAN常见的训练挑战与评估指标有哪些？
• RQ3 与合成时间序列数据相关的隐私风险与保护措施有哪些？
• RQ4 时间序列GAN研究中使用了哪些数据集与基准，标准化方面存在哪些差距？

主要发现

• 提出一个两部分分类法：离散变体与连续变体时间序列GAN。
• 综合了主导时间序列GAN模型的架构、损失与应用。
• 强调训练稳定性、评估与隐私作为时间序列GAN的核心挑战。
• 指出时间序列GAN缺乏标准化的基准数据集和评估指标。
• 记录文献中评审的流行数据集和实际应用。
• 回顾用于合成数据的隐私保护方法和监管考虑。