Graphical Modeling Of High-Dimensional Time Series
Graphical Modeling Of High-Dimensional Time Series
复制标题
高维时间序列的图形建模
DOI：
10.1109/acssc.2018.8645324
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Jitendra Tugnait
中科院分区：
文献类型：
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作者：
Jitendra Tugnait
研究了高维平稳多元实值高斯时间序列的条件独立图的推断问题。一个p-变量高斯时间序列图模型与一个无向图有p个顶点被定义为族的时间序列，服从的条件独立的限制所隐含的边集的图。我们提出了一种新的制定联合图形套索频域，适用于相关的时间序列，推广目前的时域方法，i.i.d.时间序列该方法是非参数的。首先在频域中构造一个充分统计量集，然后对该充分统计量集的惩罚对数似然进行优化。提出了一种基于交替极小化的优化算法，并通过数值算例进行了说明。
We consider the problem of inferring the conditional independence graph of a high-dimensional stationary multivariate real-valued Gaussian time series. A p-variate Gaussian time series graphical model associated with an undirected graph with p vertices is defined as the family of time series that obey the conditional independence restrictions implied by the edge set of the graph. We present a novel formulation of joint graphical lasso in frequency domain, suitable for dependent time series, generalizing current time-domain approaches to i.i.d. time series. The approach is nonparametric. First a sufficient statistic set in frequency domain is developed, and then a penalized log-likelihood of the sufficient statistic set is optimized. An optimization algorithm based on alternating minimization is presented and illustrated via numerical examples.