论文标题
使用公共源协调GAN（COSCI-GAN）生成多元时间序列
Generating multivariate time series with COmmon Source CoordInated GAN (COSCI-GAN)
论文作者
论文摘要
生成多元时间序列是在许多医学，财务和物联网应用程序中共享敏感数据的有前途的方法。多元时间序列的常见类型源自单个来源，例如医疗患者的生物特征测量。这导致了单个时间序列之间的复杂动力学模式，这些模式很难通过典型的生成模型（例如gan）学习。在这些模式中，机器学习模型可以用来更好地分类，预测或执行其他下游任务。我们提出了一个新颖的框架，该框架将时间序列的共同起源考虑在内，并有利于渠道/特征关系保存。我们方法的两个关键点是：1）单个时间序列是从潜在空间中的一个共同点生成的，2）中央歧视器有利于保存通道间/特征动力学。我们从经验上证明我们的方法有助于保留渠道/特征相关性，并且我们的合成数据在使用医疗和财务数据的下游任务中表现出色。
Generating multivariate time series is a promising approach for sharing sensitive data in many medical, financial, and IoT applications. A common type of multivariate time series originates from a single source such as the biometric measurements from a medical patient. This leads to complex dynamical patterns between individual time series that are hard to learn by typical generation models such as GANs. There is valuable information in those patterns that machine learning models can use to better classify, predict or perform other downstream tasks. We propose a novel framework that takes time series' common origin into account and favors channel/feature relationships preservation. The two key points of our method are: 1) the individual time series are generated from a common point in latent space and 2) a central discriminator favors the preservation of inter-channel/feature dynamics. We demonstrate empirically that our method helps preserve channel/feature correlations and that our synthetic data performs very well in downstream tasks with medical and financial data.
