Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 33 - 61 |
| Number of pages | 29 |
| Journal | Fuzzy Sets and Systems |
| Volume | 193 |
| DOIs | |
| Publication status | Published - 2012 |
Cite this
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Wavelets-based clustering of multivariate time series. / D'Urso, Pierpaolo; Maharaj, Elizabeth Ann.
In: Fuzzy Sets and Systems, Vol. 193, 2012, p. 33 - 61.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Wavelets-based clustering of multivariate time series
AU - D'Urso, Pierpaolo
AU - Maharaj, Elizabeth Ann
PY - 2012
Y1 - 2012
N2 - Crisp and fuzzy clustering methods based on a combination of univariate and multivariate wavelet features are considered for the clustering of multivariate time series. The performance of each of these methods is evaluated for stationary and variance nonstationary multivariate time series with different error correlation structures. The main outcomes of the simulation studies are are as follows: the superior performance of this approach for both the crisp and fuzzy cluster methods compared to some of the other approaches for clustering multivariate time series; the very good performance of the fuzzy relational method, overall, to cluster longer time series when all of them do not appear to group exclusively into well separated clusters. We consider an application to multivariate greenhouse gases time series and show that the crisp and fuzzy clustering methods considered are well validated.
AB - Crisp and fuzzy clustering methods based on a combination of univariate and multivariate wavelet features are considered for the clustering of multivariate time series. The performance of each of these methods is evaluated for stationary and variance nonstationary multivariate time series with different error correlation structures. The main outcomes of the simulation studies are are as follows: the superior performance of this approach for both the crisp and fuzzy cluster methods compared to some of the other approaches for clustering multivariate time series; the very good performance of the fuzzy relational method, overall, to cluster longer time series when all of them do not appear to group exclusively into well separated clusters. We consider an application to multivariate greenhouse gases time series and show that the crisp and fuzzy clustering methods considered are well validated.
U2 - 10.1016/j.fss.2011.10.002
DO - 10.1016/j.fss.2011.10.002
M3 - Article
VL - 193
SP - 33
EP - 61
JO - Fuzzy Sets and Systems
JF - Fuzzy Sets and Systems
SN - 0165-0114
ER -