语言时间序列年月日_TIGRAMITE | 基于Python语言开发的时间序列因果分析软件包！...
今日推荐一个基于Python 语言开发的时间序列因果分析软件包：
TIGRAMITE – CAUSAL DISCOVERY FOR TIME SERIES DATASETS
Tigramite是一个因果时间序列分析python软件包。
它可以从高维时间序列数据集中有效地重构因果关系图，为因果中介和预测分析建模中所获得的因果依存关系提供判断依据。
软件包提供的因果发现基于线性及非参数条件独立性测试，适用于离散或连续值时间序列对象。
当前，TIGRAMITE无法识别有向的即刻链接的因果指向性。
本软件包还包括用于高质量结果的图形输出功能。
项目官方地址：
https://tocsy.pik-potsdam.de/tigramite.php
Version 4.1 described in
https://advances.sciencemag.org/content/5/11/eaau4996
(Python Package)
Github 代码地址：
https://github.com/jakobrunge/tigramite.git
Documentation 文档地址：
https://jakobrunge.github.io/tigramite/
General Notes
Tigramite is a causal time series analysis python package.
It allows to efficiently reconstruct causal graphs from high-dimensional time series datasets and model the obtained causal dependencies for causal mediation and prediction analyses.
Causal discovery is based on linear as well as non-parametric conditional independence tests applicable to discrete or continuously-valued time series.
Currently, tigramite cannot identify causal directionality for contemporaneous links which are left undirected.
Also includes functions for high-quality plots of the results.
Please cite the following papers depending on which method you use:
PCMCI: J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic, Detecting and quantifying causal associations in large nonlinear time series datasets. Sci. Adv. 5, eaau4996 (2019) . https://advances.sciencemag.org/content/5/11/eaau4996
Generally: J. Runge (2018): Causal Network Reconstruction from Time Series: From Theoretical Assumptions to Practical Estimation. Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7): 075310.
https://aip.scitation.org/doi/10.1063/1.5025050
Nature Communications Perspective paper:
https://www.nature.com/articles/s41467-019-10105-3
Mediation class: J. Runge et al. (2015): Identifying causal gateways and mediators in complex spatio-temporal systems. Nature Communications, 6, 8502.
http://doi.org/10.1038/ncomms9502
Mediation class: J. Runge (2015): Quantifying information transfer and mediation along causal pathways in complex systems. Phys. Rev. E, 92(6), 62829.
http://doi.org/10.1103/PhysRevE.92.062829
CMIknn: J. Runge (2018): Conditional Independence Testing Based on a Nearest-Neighbor Estimator of Conditional Mutual Information. In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics.
http://proceedings.mlr.press/v84/runge18a.html
Features
high detection power even for large-scale time series datasets
flexible conditional independence test statistics adapted to continuously-valued or discrete data, and different assumptions about linear or nonlinear dependencies
automatic hyperparameter optimization for most tests
parallel computing script based on mpi4py
handling of missing values and masks
p-value correction and confidence interval estimation
causal mediation class to analyze causal pathways
prediction class based on sklearn models including causal feature selection
currently, tigramite cannot identify causal directionality for contemporaneous links which are left undirected
Required python packages
numpy>=1.10.0
scipy>=0.17.0
scikit-learn>=0.18.1 (optional, necessary for GPDC test)
matplotlib>=1.5.1 (optional, only for plotting)
networkx=1.10.0 (optional, only for plotting and mediation)
cython>=0.26 (optional, necessary for CMIknn and GPDC tests)
mpi4py>=2.0.0 (optional, necessary for using the parallelized implementation)
rpy2>=2.8 (optional, necessary for RCOT test)
Installation
python setup.py install
This will install tigramite in your path.
To use just the ParCorr and CMIsymb independence tests, only numpy and scipy are required. For other independence tests more packages are required:
CMIknn: cython can optionally be used for compilation, otherwise the provided ``*.c'' file is used
GPDC: also based on cython, and additionally, scikit-learn is required for Gaussian Process regression
RCOT requires more work: Firstly, rpy2 is required to access R-packages. The required R-packages can be installed with the script ``install_r_packages.sh''. Due to R-related issues, the installation of R's devtools might fail when running "install_r_packages.sh". This can be solved e.g. by installing missing / not resolved R dependencies using conda: "conda install r-git2r".
User Agreement
By downloading TIGRAMITE you agree with the following points: TIGRAMITE is provided without any warranty or conditions of any kind. We assume no responsibility for errors or omissions in the results and interpretations following from application of TIGRAMITE.
You commit to cite above papers in your reports or publications.
License
Copyright (C) 2014-2019 Jakob Runge
See license.txt for full text.
TIGRAMITE is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version.
TIGRAMITE is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
作者：
Authors
Jakob Runge
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转载自：https://github.com/jakobrunge/tigramite、点击
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