Change-point detection of climate time series by nonparametric method
- In one of the data mining techniques, change-point detection is of importance in evaluating time series measured in real world. For decades this technique has been developed as a nonlinear dynamics. We apply the method for detecting the change points, Singular Spectrum Transformation (SST), to the climate time series. To know where the structures of climate data sets change can reveal a climate background. In this paper we discuss the structures of precipitation data in Kenya and Wrangel Island (Arctic land) by using the SST.
| Author details: | Naoki Itoh, Jürgen KurthsORCiDGND |
|---|---|
| URL: | http://www.doaj.org/doaj?func=openurl&issn=20780958&genre=journal |
| ISSN: | 2078-0958 |
| Publication type: | Article |
| Language: | English |
| Year of first publication: | 2010 |
| Publication year: | 2010 |
| Release date: | 2017/03/25 |
| Source: | Lecture notes in engineering and computer science. - ISSN 2078-0958. - 2186 (2010), 1, S. 445 - 448 |
| Organizational units: | Zentrale und wissenschaftliche Einrichtungen / Interdisziplinäres Zentrum für Dynamik komplexer Systeme |
| Peer review: | Referiert |
