TOCSY - Toolboxes for Complex Systems: Tigramite is a time series analysis python module. With flexibly adaptable scripts it allows to reconstruct graphical models (conditional independence graphs) from discrete or continuously-valued time series based on a causal discovery algorithm, quantify interaction strengths with different measures, and create high-quality plots of the results. Features: Analysis can be performed on multivariate time series. Further scripts allow sliding window or ensemble analyses; Functions for custom preprocessing like anomalization, high/lowpass filters, masking of samples (e.g. winter months only), time-binning, ordinal pattern analysis, and more; Different (conditional) measures of association (partial correlation, standardized regression, and conditional mutual information with different estimators); Fast computation through use of Cython; also fully parallelized script (mpi4py package necessary) available; Significance testing via analytical tests or a shuffle test for conditional mutual information; Flexible plotting scripts for publication quality presentation of results.
References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Saggioro, Elena; de Wiljes, Jana; Kretschmer, Marlene; Runge, Jakob: Reconstructing regime-dependent causal relationships from observational time series (2020)
- Runge, J.: Causal network reconstruction from time series: from theoretical assumptions to practical estimation (2018)