Synthetic Time Series for Clustering Performace Comparisons.

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Description

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

Usage

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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 = ≤ft( 0.3 -0.2 \varepsilon_{t-1} \right) X_{t-1} + 1.0 +\varepsilon_{t}
M3 EXPAR X_t =≤ft( 0.9 \exp ≤ft( - X_{t-1}^2 \right) -0.6 \right) X_{t-1} + 1.0 + \varepsilon_{t}
M4 SETAR X_t =≤ft( 0.3 X_{t-1} +1.0 \right) I ≤ft( X_{t-1} ≥q 0.2 \right) -
≤ft( 0.3 X_{t-1} -1.0 \right) I ≤ft( X_{t-1} < 0.2 \right) + \varepsilon_{t}
M5 NLAR X_t = 0.7 ≤ft| X_{t-1} \right| ≤ft( 2 + ≤ft| X_{t-1} \right| \right)^{-1} + \varepsilon_{t}
M6 STAR X_t = 0.8 X_{t-1} -0.8 X_{t-1} ≤ft( 1 + \exp ≤ft( -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. http://www.jstatsoft.org/v62/i01/.

Examples

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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 )$clustering #use pam clustering to form 6 clusters
cluster.evaluation( true_cluster, acfcluster)
## Not run: 
#test another dissimilarity metric and a cluster algorithm
chernoffdist <- diss( synthetic.tseries, "SPEC.LLR")
chernoffclust <- pam( chernoffdist, 6 )$clustering 
cluster.evaluation( true_cluster, chernoffclust)

## End(Not run)

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