论文标题
部分可观测时空混沌系统的无模型预测
Distribution estimation and change-point estimation for time series via DNN-based GANs
论文作者
论文摘要
生成对抗网络（GAN）最近已应用于估计独立和相同分布数据的分布，并引起了很多研究的关注。在本文中，我们使用阻止技术来证明gans在估计固定时间序列的分布方面的有效性。从理论上讲，我们为深度神经网络（DNN）基于时间序列的固定分布的基于深神经网络（DNN）的gans估计量绑定了一个非反应误差。基于我们的理论分析，我们提出了一种用于估计时间序列分布的变化点的算法。通过两个蒙特卡洛实验验证了这两个主要结果，一种是估计20尺寸AR（3）模型的$ 5 $ tuple样品的联合固定分布，另一个是关于在两个不同固定时间序列的组合下估计变化点。现实世界对人类活动识别数据集的经验应用突出了所提出方法的潜力。
The generative adversarial networks (GANs) have recently been applied to estimating the distribution of independent and identically distributed data, and have attracted a lot of research attention. In this paper, we use the blocking technique to demonstrate the effectiveness of GANs for estimating the distribution of stationary time series. Theoretically, we derive a non-asymptotic error bound for the Deep Neural Network (DNN)-based GANs estimator for the stationary distribution of the time series. Based on our theoretical analysis, we propose an algorithm for estimating the change point in time series distribution. The two main results are verified by two Monte Carlo experiments respectively, one is to estimate the joint stationary distribution of $5$-tuple samples of a 20 dimensional AR(3) model, the other is about estimating the change point at the combination of two different stationary time series. A real world empirical application to the human activity recognition dataset highlights the potential of the proposed methods.
