VLTimeCausality: 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) <https://www.cs.uic.edu/~elena/pubs/amornbunchornvej-dsaa19.pdf> when referring to this package in publications.

Package details

AuthorChainarong Amornbunchornvej [aut, cre] (<https://orcid.org/0000-0003-3131-0370>)
MaintainerChainarong Amornbunchornvej <[email protected]>
URL https://github.com/DarkEyes/VLTimeSeriesCausality
Package repositoryView on CRAN
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VLTimeCausality documentation built on Dec. 28, 2019, 9:06 a.m.