Pattern Causality between two series

Cross validation

As an example, we could import climate from patterncausality package.

library(patterncausality)
data(climate_indices)

Then we consider a method of cross-validation to see the robustness of pattern causality.

set.seed(123)
X <- climate_indices$PNA
Y <- climate_indices$NAO
numberset <- c(100,200,300,400,500)
result <- pcCrossValidation(X,Y,3,2,"euclidean",1,FALSE,numberset)
#>   |                                                                              |                                                                      |   0%  |                                                                              |##################                                                    |  25%  |                                                                              |###################################                                   |  50%  |                                                                              |####################################################                  |  75%  |                                                                              |######################################################################| 100%
print(result)
#>      positive  negative      dark
#> 100 0.1000000 0.4500000 0.4500000
#> 200 0.3488372 0.1860465 0.4651163
#> 300 0.4868421 0.2368421 0.2763158
#> 400 0.3636364 0.1818182 0.4545455
#> 500 0.3687943 0.2695035 0.3617021

In order to make the results better observed, we provide the plotCV function to give a line chart of the result.

plotCV(result)

We can find that the causality keep the same position when the sample number is large enough, this method indeed captures hidden patterns and causal connections in sequences.

Pattern Causality between two series

Stavros Stavroglou, Athanasios Pantelous, Hui Wang

Cross validation