A framework to infer causality on a pair of time series of real numbers based on Variable-lag Granger causality (VL-Granger) and transfer entropy (VL-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.
For the newest version on github, please call the following command in R terminal.
This requires a user to install the "remotes" package before installing VLTimeSeriesCausality.
In the first step, we generate time series TS$X and TS$Y where TS$X causes TS$Y with variable-lags.
library(VLTimeSeriesCausality) # Generate simulation data TS <- VLTimeCausality::SimpleSimulationVLtimeseries()
We can plot time series using the following function.
A sample of generated time series pair that has a causal relation is plotted below:
We use the following function to infer whether X causes Y.
# Run the function out<-VLTimeCausality::VLGrangerFunc(Y=TS$Y,X=TS$X)
The result of Granger causality is below:
out$BICDiffRatio  0.8882051 out$XgCsY  TRUE
Chainarong Amornbunchornvej, Elena Zheleva, and Tanya Berger-Wolf (2019). Variable-lag Granger Causality for Time Series Analysis. In Proceedings of the 6th IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 21-30. IEEE. link
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