It is abundantly clear that time dependent data is a vital source of information in the world. The challenge has been for applications in machine learning to gain access to a considerable amount of quality data needed for algorithm development and analysis. Modeling synthetic data using a Generative Adversarial Network (GAN) has been at the heart of providing a viable solution. Our work focuses on one dimensional times series and explores the few shot approach, which is the ability of an algorithm to perform well with limited data. This work attempts to ease the frustration by proposing a new architecture, Time Series GAN (TSGAN), to model realistic time series data. We evaluate TSGAN on 70 data sets from a benchmark time series database. Our results demonstrate that TSGAN performs better than the competition both quantitatively using the Frechet Inception Score (FID) metric, and qualitatively when classification is used as the evaluation criteria.

中文翻译：

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用于时间序列生成的条件 GAN

很明显，时间相关数据是世界上重要的信息来源。机器学习中的应用程序面临的挑战是获得算法开发和分析所需的大量高质量数据。使用生成对抗网络 (GAN) 对合成数据进行建模一直是提供可行解决方案的核心。我们的工作侧重于一维时间序列，并探索了少镜头方法，这是一种算法在有限数据下表现良好的能力。这项工作试图通过提出一种新的架构时间序列 GAN (TSGAN) 来对现实的时间序列数据进行建模来缓解这种挫败感。我们在来自基准时间序列数据库的 70 个数据集上评估 TSGAN。