A two-stage causality method for time series prediction based on feature selection and momentary conditional independence

DOI10.1016/j.physa.2022.126970OpenAlexW4210263173MaRDI QIDQ2128654

Weijie Ren, Dewei Ma, Min Han

Publication date: 22 April 2022

Published in: Physica A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physa.2022.126970

zbMATH Keywords

causalityfeature selectionmultivariate time series predictionglobal redundancy minimizationmomentary conditional independence

Mathematics Subject Classification ID

Statistical mechanics, structure of matter (82-XX)

Uses Software

• TETRAD
• SSA

Cites Work

• Detecting Causality in Complex Ecosystems
• Causation, prediction, and search
• A novel Granger causality method based on HSIC-Lasso for revealing nonlinear relationship between multivariate time series
• Measurement of Linear Dependence and Feedback Between Multiple Time Series
• A General Framework for Auto-Weighted Feature Selection via Global Redundancy Minimization
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