Compressive Nonparametric Graphical Model Selection For Time Series
We propose a method for inferring the conditional indepen- dence graph (CIG) of a high-dimensional discrete-time Gaus- sian vector random process from finite-length observations. Our approach does not rely on a parametric model (such as, e.g., an autoregressive model) for the vector random process; rather, it only assumes certain spectral smoothness proper- ties. The proposed inference scheme is compressive in that it works for sample sizes that are (much) smaller than the number of scalar process components. We provide analytical conditions for our method to correctly identify the CIG with high probability.
Code (0)
등록된 구현이 없습니다.
Tasks
Model SelectionTime SeriesTime Series AnalysisSimilar Papers 제목 키워드 기반
Graphical LASSO Based Model Selection for Time Series
We propose a novel graphical model selection (GMS) scheme for high-dimensional stationary time series or discrete time process. The method is based on a natural generalization of the graphical LASSO (gLASSO), introduced …
Gaussian ProcessesModel SelectionTime SeriesTime Series AnalysisNonparametric undirected graphical model selection using diffusion models
Undirected graphical models provide a fundamental framework for representing conditional independence structures among high-dimensional random variables. While undirected graphical model selection has become a central pr…
Conditional independence testing with a single realization of a multivariate nonstationary nonlinear time series
Identifying relationships among stochastic processes is a key goal in disciplines that deal with complex temporal systems, such as economics. While the standard toolkit for multivariate time series analysis has many adva…
Causal DiscoveryTime SeriesTime Series AnalysisVariable SelectionGraphical Models for Financial Time Series and Portfolio Selection
We examine a variety of graphical models to construct optimal portfolios. Graphical models such as PCA-KMeans, autoencoders, dynamic clustering, and structural learning can capture the time varying patterns in the covari…
Asset ManagementClusteringManagementTime Series+1Variable Selection for Nonparametric Learning with Power Series Kernels
In this paper, we propose a variable selection method for general nonparametric kernel-based estimation. The proposed method consists of two-stage estimation: (1) construct a consistent estimator of the target function, …
Density Ratio EstimationregressionVariable Selection