论文标题
有条件的gan生成时间
Conditional GAN for timeseries generation
论文作者
论文摘要
很明显，时间依赖数据是世界上重要的信息来源。挑战是在机器学习中的应用程序，以获取算法开发和分析所需的相当数量的质量数据。使用生成对抗网络（GAN）对合成数据进行建模是提供可行解决方案的核心。我们的工作着重于一维时序列，并探讨了几种射击方法，这是算法在有限数据方面表现良好的能力。这项工作试图通过提出新的架构时间序列GAN（TSGAN）来模拟现实的时间序列数据来减轻挫败感。我们从基准时间序列数据库中评估了70个数据集的TSGAN。我们的结果表明，使用Frechet Inception评分（FID）度量，以及当将分类用作评估标准时，TSGAN的性能均优于竞争。
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.
