GRAPHICAL MODELLING OF MULTIVARIATE TIME SERIES WITH LATENT VARIABLES
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Summary
This paper describes a new graphical approach for modelling the dependence structure of mul- tivariate stationary time series that are afiected by latent variables based on mixed graphs, and shows that these models can be viewed as graphical ARMA models that satisfy the Granger causality restrictions encoded by general mixed graphs.
- Type
- article
- Published
- 2006-01-01
- Cited by
- 18
- References
- 34
- OpenAlex
- https://openalex.org/W79887217
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:116825225
Keywords
Spurious relationship, Latent variable, Graphical model, Autoregressive model, Granger causality
References
- A graphical approach for evaluating effective connectivity in neural systems
- Autoregressive modeling and causal ordering of economic variables
- An Approximate Inverse for the Covariance Matrix of Moving Average and Autoregressive Processes
- Estimation and information in stationary time series
- Asymptotically Efficient Estimation of Covariance Matrices with Linear Structure
- Structural Equation Models in the Social Sciences.
- Markov Properties for Acyclic Directed Mixed Graphs
- STRUCTURAL EQUATION METHODS IN THE SOCIAL SCIENCES
- Using Path Diagrams as a Structural Equation Modeling Tool
- Causal diagrams for empirical research
- Approximations for Stationary Covariance Matrices and Their Inverses with Application to ARMA Models
- A New Identification Condition for Recursive Models With Correlated Errors
- The Method of Path Coefficients
- Granger causality and path diagrams for multivariate time series
- Multivariate Dependencies: Models, Analysis and Interpretation
- Ancestral graph Markov models
- The Factorization of Matricial Spectral Densities
- Time Series: Theory and Methods
- On the Validity of the Markov Interpretation of Path Diagrams of Gaussian Structural Equations Systems with Correlated Errors
- Graphical modelling of multivariate time series
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- Temporal causal modeling with graphical granger methods
- A Sparsification Approach for Temporal Graphical Model Decomposition
- Graphical modelling of multivariate time series
- High-order dynamic Bayesian Network learning with hidden common causes for causal gene regulatory network
- Time Delayed Causal Gene Regulatory Network Inference with Hidden Common Causes
- Fast sparse subspace identification tools with applications to dynamic vision
- Large Scale Data Mining for IT Service Management
- Evaluation of Causal Inference Techniques for AIOps
- Causal Modeling based Fault Localization in Cloud Systems using Golden Signals
- Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning
- Online inference for time-varying temporal dependency discovery from time series
- Reviewing Graphical Modelling of Multivariate Temporal Processes
- Related Work on Geometry of Non-Convex Programs
- Exploiting Temporal Structure for Causal Modeling
- 2016 Ieee International Conference on Big Data (big Data) Online Inference for Time-varying Temporal Dependency Discovery from Time Series
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