A novel Granger causality method based on HSIC-Lasso for revealing nonlinear relationship between multivariate time series
From MaRDI portal
Publication:2137578
DOI10.1016/j.physa.2019.123245OpenAlexW2980578704WikidataQ130541842 ScholiaQ130541842MaRDI QIDQ2137578
Weijie Ren, Baisong Li, Min Han
Publication date: 16 May 2022
Published in: Physica A (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.physa.2019.123245
nonlinear systemmultivariate time seriesHilbert-Schmidt independence criterionGranger causality analysis
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Simulation study of direct causality measures in multivariate time series
- Multivariate linear and nonlinear causality tests
- A new statistic and practical guidelines for nonparametric Granger causality testing
- Behavior of the standard Dickey-Fuller test when there is a Fourier-form break under the null hypothesis
- State space reconstruction parameters in the analysis of chaotic time series - the role of the time window length
- Nonlinear dynamics delay times, and embedding windows
- Measurement of Linear Dependence and Feedback Between Multiple Time Series
- Causal Network Inference Via Group Sparse Regularization
- Granger Causality in Multivariate Time Series Using a Time-Ordered Restricted Vector Autoregressive Model
- On the Efficacy of State Space Reconstruction Methods in Determining Causality
- Regularization Parameter Selections via Generalized Information Criterion
- Investigating Causal Relations by Econometric Models and Cross-spectral Methods
- High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso
- Algorithmic Learning Theory
This page was built for publication: A novel Granger causality method based on HSIC-Lasso for revealing nonlinear relationship between multivariate time series