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Trend Analysis Using Non-stationary Time Series Clustering Based on the Finite Element Method : Volume 21, Issue 3 (23/05/2014)

By Gorji Sefidmazgi, M.

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Book Id: WPLBN0003992625
Format Type: PDF Article :
File Size: Pages 11
Reproduction Date: 2015

Title: Trend Analysis Using Non-stationary Time Series Clustering Based on the Finite Element Method : Volume 21, Issue 3 (23/05/2014)  
Author: Gorji Sefidmazgi, M.
Volume: Vol. 21, Issue 3
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2014
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Sefidmazgi, M. G., Homaifar, A., Liess, S., Jha, M. K., & Sayemuzzaman, M. (2014). Trend Analysis Using Non-stationary Time Series Clustering Based on the Finite Element Method : Volume 21, Issue 3 (23/05/2014). Retrieved from http://hawaiilibrary.net/


Description
Description: North Carolina A&T State University, Dept. of Electrical Engineering, Greensboro, USA. 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.

Summary
Trend analysis using non-stationary time series clustering based on the finite element method

Excerpt
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