A carregar...
Publicação
Independent Component Analysis for Extended Time Series in Climate Data
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 185.26 KB | Adobe PDF |
Orientador(es)
Resumo(s)
Various techniques of multivariate data analysis have been proposed to
study time series, including the Multi-channel Singular Spectrum Analysis (MSSA).
This technique is a Principal Component Analysis (PCA) of the extended matrix of
initial lagged series, also called Extended Empirical Orthogonal Function (EEOF)
Analysis in a climatological context. This work uses Independent Component Analysis
(ICA) as an alternative to the MSSA method, when studying the extended
time series matrix. Often, ICA is more appropriate than PCA to analyse time series,
since the extraction of Independent Components (ICs) involves higher-order
statistics whereas PCA only uses the second-order statistics to obtain the Principal
Components (PCs), which are not correlated and are not necessarily independent.
An example of time series for meteorological data and some comparative results
between the techniques under study are given. Different methods of ordering ICs
are also presented, including a new one, which may influence the quality of the
reconstruction of the original data.
Descrição
Palavras-chave
Independent Component Analysis Principal Component Analysis Multi-channel Singular Spectrum Analysis Time Series Climate Data
Contexto Educativo
Citação
Sebastião, F. and Oliveira, I. (2013). Independent Component Analysis for Extended Time Series in Climate Data, Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications. Studies in Theoretical and Applied Statistics, 427-436.
Editora
Springer Berlin Heidelberg