 |
| Titel |
Trend analysis using non-stationary time series clustering based on the finite element method |
| VerfasserIn |
M. Gorji Sefidmazgi, M. Sayemuzzaman, A. Homaifar, M. K. Jha, S. Liess |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1023-5809
|
| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 21, no. 3 ; Nr. 21, no. 3 (2014-05-23), S.605-615 |
| Datensatznummer |
250120916
|
| Publikation (Nr.) |
 copernicus.org/npg-21-605-2014.pdf |
|
|
|
|
|
| Zusammenfassung |
| In order to analyze low-frequency variability of climate, it is useful to
model the climatic time series with multiple linear trends and locate the
times of significant changes. In this paper, we have used non-stationary time
series clustering to find change points in the trends. Clustering in a
multi-dimensional non-stationary time series is challenging, since the
problem is mathematically ill-posed. Clustering based on the finite element
method (FEM) is one of the methods that can analyze multidimensional time
series. One important attribute of this method is that it is not dependent on
any statistical assumption and does not need local stationarity in the time
series. In this paper, it is shown how the FEM-clustering method can be used
to locate change points in the trend of temperature time series from in
situ observations. This method is applied to the temperature time
series of North Carolina (NC) and the results represent region-specific
climate variability despite higher frequency harmonics in climatic time
series. Next, we investigated the relationship between the climatic indices
with the clusters/trends detected based on this clustering method. It appears
that the natural variability of climate change in NC during 1950–2009 can be
explained mostly by AMO and solar activity. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|