基于数据的噪声时间序列的建模与卷积生成对抗网络
1Communications Technology Laboratory, National Institute of Standards and Technology, Boulder, CO 80305, United States of America.
概括
生成对抗性网络 (GAN) 可以在时间序列数据中学习许多类型的随机噪声,但与冲动噪声作斗争. 这项研究对信号处理中的噪声建模的GAN性能进行了基准测试.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 数据分析 数据分析
背景情况:
- 随机噪声是物理测量的固有因素,限制了信号处理.
- 生成对抗网络 (GAN) 显示出数据驱动建模的前景.
- 评估GANs在时间序列中复制噪声的能力至关重要.
研究的目的:
- 实证地研究GAN在时间序列数据中忠实地重现各种噪音类型的能力.
- 评估两个深度卷积GAN架构的时间序列噪声生成.
- 提供对噪声建模GAN限制的见解,并为未来的研究建立一个基准.
主要方法:
- 在模拟噪声上训练和评估了两个通用时间序列GAN (直接和基于图像的).
- 用了基于图像的GAN数据表示的短时间里叶变换.
- 在各种噪声分布上测试了GAN:带限热,功率定律,射击和冲动噪声.
主要成果:
- GANs成功地学习了几种噪音类型,证明了噪音建模的能力.
- 与架构不合适的噪音类型降低了GAN性能,例如极端异常值的冲动噪声.
- 该研究确定了当前时间序列GANs的特定局限性.
结论:
- GAN显示了模拟各种时间序列噪声特征的潜力.
- 建筑适用性是GAN准确复制复杂噪声模式的关键.
- 这项工作为开发时间序列噪声的先进深度生成模型提供了一个基准.
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