Causal network reconstruction from time series: From theoretical assumptions to practical estimation

DOI10.1063/1.5025050zbMath1396.62218OpenAlexW2884025810WikidataQ64123107 ScholiaQ64123107MaRDI QIDQ4683673

Jakob Runge

Publication date: 21 September 2018

Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1063/1.5025050

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics (62P99)

Related Items

Mutual-information-inspired heuristics for constraint-based causal structure learning ⋮ Reconstructing regime-dependent causal relationships from observational time series ⋮ Shortcomings of Transfer Entropy and Partial Transfer Entropy: Extending Them to Escape the Curse of Dimensionality ⋮ Demand forecasting of individual probability density functions with machine learning ⋮ Improving on transfer entropy-based network reconstruction using time-delays: Approach and validation ⋮ Transfer entropy on symbolic recurrences ⋮ Topological features determining the error in the inference of networks using transfer entropy

Uses Software

• pcalg
• TETRAD
• Tigramite

Cites Work

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