论文标题
具有生成神经网络的多元时间序列建模
Multivariate time-series modeling with generative neural networks
论文作者
论文摘要
引入了生成力矩匹配网络（GMMN）作为多元时间序列（MTS）关节创新分布的依赖模型。遵循流行的Copula-Garch方法来建模依赖MTS数据，提出了基于GMMN-Garch方法的框架。首先，使用ARMA-GARCH模型来捕获每个单变量边缘时间序列中的串行依赖性。其次，如果边际时间序列的数量很大，则主成分分析（PCA）被用作减小步骤。最后，剩余的横截面依赖性是通过GMMN建模的，GMMN是这项工作的主要贡献。 GMMN非常灵活，易于模拟，这比Copula-garch方法是一个主要优势。涉及产量曲线建模和外汇率回报分析的应用表明了GMMN-GARCH方法的实用性，尤其是在产生更好的经验预测分布并进行更好的概率预测方面。
Generative moment matching networks (GMMNs) are introduced as dependence models for the joint innovation distribution of multivariate time series (MTS). Following the popular copula-GARCH approach for modeling dependent MTS data, a framework based on a GMMN-GARCH approach is presented. First, ARMA-GARCH models are utilized to capture the serial dependence within each univariate marginal time series. Second, if the number of marginal time series is large, principal component analysis (PCA) is used as a dimension-reduction step. Last, the remaining cross-sectional dependence is modeled via a GMMN, the main contribution of this work. GMMNs are highly flexible and easy to simulate from, which is a major advantage over the copula-GARCH approach. Applications involving yield curve modeling and the analysis of foreign exchange-rate returns demonstrate the utility of the GMMN-GARCH approach, especially in terms of producing better empirical predictive distributions and making better probabilistic forecasts.
