TSclust (version 1.1)

synthetic.tseries: Synthetic Time Series for Clustering Performace Comparisons.

Description

This dataset features three repetitions of several models of time series.

Usage

synthetic.tseries

Details

The dataset is formed by several repetitions of each of the following models. cll{ M1 AR $X_t = 0.6 X_{t-1} + \varepsilon_{t}$ M2 Bilinear $X_t = \left( 0.3 -0.2 \varepsilon_{t-1} \right) X_{t-1} + 1.0 +\varepsilon_{t}$ M3 EXPAR $X_t =\left( 0.9 \exp \left( - X_{t-1}^2 \right) -0.6 \right) X_{t-1} + 1.0 + \varepsilon_{t}$ M4 SETAR $X_t =\left( 0.3 X_{t-1} +1.0 \right) I \left( X_{t-1} \geq 0.2 \right) -$ $\left( 0.3 X_{t-1} -1.0 \right) I \left( X_{t-1} < 0.2 \right) + \varepsilon_{t}$ M5 NLAR $X_t = 0.7 \left| X_{t-1} \right| \left( 2 + \left| X_{t-1} \right| \right)^{-1} + \varepsilon_{t}$ M6 STAR $X_t = 0.8 X_{t-1} -0.8 X_{t-1} \left( 1 + \exp \left( -10 X_{t-1} \right) \right)^{-1} + \varepsilon_{t}$ }

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.

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 <- proxy::dist( synthetic.tseries, diss.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
if (require(proxy)) {
  acfdist <- proxy::dist( synthetic.tseries, diss.ACF, p=0.05) 
  acfcluster <- pam( acfdist, 6 )$clustering #use pam clustering to form 6 clusters
  cluster.evaluation( true_cluster, acfcluster)
#test another dissimilarity metric and a cluster algorithm
  chernoffdist <- proxy::dist( synthetic.tseries, diss.SPEC.LLR)
  chernoffclust <- pam( chernoffdist, 6 )$clustering 
  cluster.evaluation( true_cluster, chernoffclust)
}