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

synthetic.tseriesR 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 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.

References

Montero, P and Vilar, J.A. (2014) TSclust: An R Package for Time Series Clustering. Journal of Statistical Software, 62(1), 1-43. \Sexpr[results=rd]{tools:::Rd_expr_doi("doi:10.18637/jss.v062.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)



TSclust documentation built on June 8, 2025, 11:05 a.m.