Variable-Lag Time Series Causality Inference Framework

A framework to infer causality on a pair of time series of real numbers based on variable-lag Granger causality and transfer entropy. Typically, Granger causality and transfer entropy have an assumption of a fixed and constant time delay between the cause and effect. However, for a non-stationary time series, this assumption is not true. For example, considering two time series of velocity of person A and person B where B follows A. At some time, B stops tying his shoes, then running to catch up A. The fixed-lag assumption is not true in this case. We propose a framework that allows variable-lags between cause and effect in Granger causality and transfer entropy to allow them to deal with variable-lag non-stationary time series. Please see Chainarong Amornbunchornvej, Elena Zheleva, and Tanya Berger-Wolf (2019) when referring to this package in publications.


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install.packages("VLTimeCausality")

0.1.1 by Chainarong Amornbunchornvej, 4 months ago


https://github.com/DarkEyes/VLTimeSeriesCausality


Report a bug at https://github.com/DarkEyes/VLTimeSeriesCausality/issues


Browse source code at https://github.com/cran/VLTimeCausality


Authors: Chainarong Amornbunchornvej [aut, cre]


Documentation:   PDF Manual  


GPL-3 license


Imports ggplot2

Depends on dtw, tseries, RTransferEntropy

Suggests knitr, rmarkdown


See at CRAN