For the at most one change point problem, we propose the use of a conceptor matrix to learn the characteristic dynamics of a specified training window in a time series.
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Nonlinear Permuted Granger Causality
Granger causal inference is a contentious but widespread method used in fields ranging from economics to neuroscience.
Description of Nonlinear Permuted Granger Causality
GitHub Link
The GitHub link is https://github.com/noahgade/nonlinearpermutedgrangercausalityIntroduce
The GitHub repository "noahgade/NonlinearPermutedGrangerCausality" contains code related to Nonlinear Permuted Granger Causality. The repository includes code for generating simulated data, performing simulations for results and figures in the associated paper, modifying application study data, and applying the method to application data. The original data for the application study was obtained from the CRCNS data sharing website. The repository also cites relevant works and comparator methods used in the paper. Granger causal inference is a contentious but widespread method used in fields ranging from economics to neuroscience.Content
Code used to obtain simulated data (.R files). Data files are located at the Google Drive folder here. Simulation code used to generate results and figures discussed in paper (.R, .cpp, and .py files). Simulation results can be found at the same Google Drive folder. Code used to modify the application study data in Section 5 (.m file). The original data for the application study was obtained from the CRCNS data shaing website. We use 060802mw02 from ac-1 and cite the following works: Machens, C. K., M. S. Wehr, and A. M. Zador (2004). Linearity of cortical receptive fields measured with natural sounds. Journal of Neuroscience 24 (5), 1089Ð1100. Asari, H., M. Wehr, C. K. Machens, and A. M. Zador (2009). Auditory cortex and thalamic neuronal responses to various natural and synthetic sounds. CRCNS.org. Code used to apply method to application data (.R files). We additionally cite the following for comparator methods in Section 4.
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