synthetic.tseries {TSclust} | R Documentation |
Synthetic Time Series for Clustering Performace Comparisons.
Description
This dataset features three repetitions of several models of time series.
Usage
data(synthetic.tseries)
Details
The dataset is a mts
object, formed by several repetitions of each of the following models.
M1 | AR | |
M2 | Bilinear | |
M3 | EXPAR | |
M4 | SETAR | |
|
||
M5 | NLAR | |
M6 | STAR | |
Three simulations of each model are included. This dataset can be used for comparing the performance of different dissimilarity measures between time series or clustering algorithms.
References
Montero, P and Vilar, J.A. (2014) TSclust: An R Package for Time Series Clustering. Journal of Statistical Software, 62(1), 1-43. http://www.jstatsoft.org/v62/i01/.
Examples
data(synthetic.tseries)
#Create the true solution, for this dataset, there are three series of each model
true_cluster <- c(1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6)
#test a dissimilarity metric and a cluster algorithm
intperdist <- diss( synthetic.tseries, "INT.PER") #create the distance matrix
#use hierarchical clustering and divide the tree in 6 clusters
intperclust <- cutree( hclust(intperdist), 6 )
#use a cluster simmilarity index to rate the solution
cluster.evaluation( true_cluster, intperclust)
#test another dissimilarity metric and a cluster algorithm
acfdist <- diss( synthetic.tseries, "ACF", p=0.05)
acfcluster <- pam( acfdist, 6 )$clustering #use pam clustering to form 6 clusters
cluster.evaluation( true_cluster, acfcluster)
#test another dissimilarity metric and a cluster algorithm
chernoffdist <- diss( synthetic.tseries, "SPEC.LLR")
chernoffclust <- pam( chernoffdist, 6 )$clustering
cluster.evaluation( true_cluster, chernoffclust)
[Package TSclust version 1.3.1 Index]