Spec: Calculate change points with spectral cluster
In SpecDetec: Change Points Detection with Spectral Clustering

Description Usage Arguments Details Value Author(s) Examples

Calculate change point based on spectral clustering you have the option to automatically calculate the number of clusters if this information is not available

1 2	Spec(data, neighboorsNumber = 5, tolerance = 0.01, maxNumberOfChangePoints = 19, estimationChangePointsNumber = NULL)

`data`	List of values corresponding to the time series
`neighboorsNumber`	Number of neighbors to consider affinity between nodes
`tolerance`	approximation to consider valid clusters, used only for calculation of forecast of change points, default 0.01
`maxNumberOfChangePoints`	maximum number of clusters for prediction : default 19
`estimationChangePointsNumber`	predicted number of change points in the series, if null, is automatically calculated: default null

Calculate change point based on spectral clustering you have the option to automatically calculate the number of clusters if this information is not available. It uses the Gaussian Kernel for the calculation of affinity matrix and Kmeans for the spectral cluster, however, several other options can be used and the package must be customized to better suit the use.

Numerical array with the position of the change points in the time series

Luis Gustavo Uzai

data <- DEVICE1[, 1]
realChangePoints <- c(which(diff(DEVICE1$Class) != 0)) 
calculateChangePoints <- Spec(data, neighboorsNumber = 6, 
         tolerance = 0.005, estimationChangePointsNumber = 2)
minValue <- -99999
maxValue <- 99999
plot(data, type = "l", xlab = "x", ylab = "y")
for (r in 1:length(realChangePoints)) {
    lines(x = c(realChangePoints[r], realChangePoints[r]), 
          y = c(minValue, maxValue), lwd = 2, col = "red")
}
for (n in 1:length(calculateChangePoints)) {
 lines(x = c(calculateChangePoints[n], calculateChangePoints[n]), 
       y = c(minValue, maxValue), lwd = 2, col = "blue")
}