Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 56-79 |
| Number of pages | 24 |
| Journal | Fuzzy Sets and Systems |
| Volume | 318 |
| DOIs | |
| Publication status | Published - Jul 2017 |
Keywords
- Fuzzy c-means clustering
- Fuzzy c-medoids clustering
- Time series data
- r-largest order statistics
- Generalised extreme value distribution
Cite this
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Fuzzy clustering of time series using extremes. / D'Urso, Pierpaolo; Maharaj, Elizabeth Ann; Alonso, Andres Modesto.
In: Fuzzy Sets and Systems, Vol. 318, 07.2017, p. 56-79.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Fuzzy clustering of time series using extremes
AU - D'Urso, Pierpaolo
AU - Maharaj, Elizabeth Ann
AU - Alonso, Andres Modesto
PY - 2017/7
Y1 - 2017/7
N2 - In this study we explore the grouping together of time series with similar seasonal patterns using extreme value analysis with fuzzy clustering. Input features into the fuzzy clustering methods are parameter estimates of time varying location, scale and shape obtained from fitting the generalised extreme value (GEV) distribution to annual maxima or the r-largest order statistics per year of the time series. An innovative contribution of the study is the development of new generalised fuzzy clustering procedures taking into account weights, and the derivation of iterative solutions based on the GEV parameter estimators. Simulation studies conducted to evaluate the methods, reveal good performance. An application is made to a set of daily sea-level time series from around the coast of Australia where the identified clusters are well validated and they can be meaningfully interpreted.
AB - In this study we explore the grouping together of time series with similar seasonal patterns using extreme value analysis with fuzzy clustering. Input features into the fuzzy clustering methods are parameter estimates of time varying location, scale and shape obtained from fitting the generalised extreme value (GEV) distribution to annual maxima or the r-largest order statistics per year of the time series. An innovative contribution of the study is the development of new generalised fuzzy clustering procedures taking into account weights, and the derivation of iterative solutions based on the GEV parameter estimators. Simulation studies conducted to evaluate the methods, reveal good performance. An application is made to a set of daily sea-level time series from around the coast of Australia where the identified clusters are well validated and they can be meaningfully interpreted.
KW - Fuzzy c-means clustering
KW - Fuzzy c-medoids clustering
KW - Time series data
KW - r-largest order statistics
KW - Generalised extreme value distribution
U2 - 10.1016/j.fss.2016.10.006
DO - 10.1016/j.fss.2016.10.006
M3 - Article
VL - 318
SP - 56
EP - 79
JO - Fuzzy Sets and Systems
JF - Fuzzy Sets and Systems
SN - 0165-0114
ER -