Author: Stavros Stavroglou, Athanasios Pantelous, Hui Wang

# Dynamic Analysis: Pattern Causality in Time Points

#### Stavros Stavroglou, Athanasios Pantelous, Hui Wang

As it’s hard to understand the pattern causality algorithm based on chaos theory, some researchers always wonder if this algorithm is able to capture the dynamic causality between series.

In this vignette, we will demonstrate how to use the `patterncausality` package to analyze the dynamic causality between two series.

First of all, we need to load the package and the dataset as usual.

    library(patterncausality)
    data(climate_indices)

Here we choose the typical climate dataset to show the dynamic analysis. We can easily get the pattern causality result by `pcLightweight` function to give a quick view, generally speaking, this result is obtained by the whole time points.

## Causality Strength Analysis with ERF Function

    X <- climate_indices$AO
    Y <- climate_indices$AAO
    result <- pcLightweight(X, Y, E = 3, tau = 1, metric = "euclidean", h = 1, weighted = TRUE, verbose=FALSE)

The parameter `weighted` decides if we need to calculate the causality strength by erf function, we can show this kind of strength here in each time point.

To get the causality strength in each time point, we need the recorded function `pcFullDetails` to give causality strength details.

    result <- pcFullDetails(X, Y, E = 3, tau = 1, metric = "euclidean", h = 1, weighted = TRUE, verbose=FALSE)
    print(result)
    #> Pattern Causality Full Analysis
    #> ------------------------------
    #> Analysis period: 8 532
    #> Number of valid observations: 525
    #> Causality spectrum range: No valid values
    #>
    #> State space dimensions: 533 x 3
    #> Pattern space dimensions: 9 x 9 x 535

The summary of the result is shown, all the related data has been saved in the `result` object, then we can plot the causality strength series from this, as we said in the previous work, the each time point if and only if the causality is one of the three types, so the each time point just has one causality.

    plot_causality(result, type="total")

We can also plot the causality strength seperately.

    plot_causality(result, type="positive")

Then it will show the single causality strength series of each status to observe the dynamic causality, since each time point just has one causality, so we use the linear interpolation to connect the points of each causality.

## Raw Causality Strength Analysis

However, sometimes we also want to choose `weighted = FALSE` to get the raw causality strength series, we can estimate the causality again by the `pcFullDetails` function.

    result <- pcFullDetails(X, Y, E = 3, tau = 1, metric = "euclidean", h = 1, weighted = FALSE, verbose=FALSE)
    print(result)
    #> Pattern Causality Full Analysis
    #> ------------------------------
    #> Analysis period: 8 532
    #> Number of valid observations: 525
    #> Causality spectrum range: No valid values
    #>
    #> State space dimensions: 533 x 3
    #> Pattern space dimensions: 9 x 9 x 535

Obviously, the number of total causality points is the same, then we also provide the plot function for this situation to find more details about the dynamic causality.

    plot_causality(result, type="total")

Then the causality strength in this situation is just 1 and 0, that’s why the figure is the bar plot, we can find the causality type in each time point and we can also obverse different causality in different time points.

These two kinds of functions extended the pattern causality to the dynamic analysis, we can find more information from the time field and the features in different time points and different causalities.