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Nonlinear multivariate and time series analysis by neural network methods

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Title: Nonlinear multivariate and time series analysis by neural network methods
Author: Hsieh, William W.
Issue Date: 2004-03-18
Publicly Available in cIRcle 2011-03-23
Publisher American Geophysical Union
Citation: Hsieh, William W. 2004. Nonlinear multivariate and time series analysis by neural network methods, Reviews of Geophysics. 42 RG1003 dx.doi.org/10.1029/2002RG000112
Abstract: Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data, data from observational arrays, from satellites, or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression at the base, followed by principal component analysis (PCA) and finally canonical correlation analysis (CCA). A multivariate time series method, the singular spectrum analysis (SSA), has been a fruitful extension of the PCA technique. The common drawback of these classical methods is that only linear structures can be correctly extracted from the data. Since the late 1980s, neural network methods have become popular for performing nonlinear regression and classification. More recently, neural network methods have been extended to perform nonlinear PCA (NLPCA), nonlinear CCA (NLCCA), and nonlinear SSA (NLSSA). This paper presents a unified view of the NLPCA, NLCCA, and NLSSA techniques and their applications to various data sets of the atmosphere and the ocean (especially for the El Niño-Southern Oscillation and the stratospheric quasi-biennial oscillation). These data sets reveal that the linear methods are often too simplistic to describe real-world systems, with a tendency to scatter a single oscillatory phenomenon into numerous unphysical modes or higher harmonics, which can be largely alleviated in the new nonlinear paradigm. An edited version of this paper was published by AGU. Copyright 2004 American Geophysical Union.
Affiliation: Earth and Ocean Sciences, Dept. of (EOS), Dept of
URI: http://hdl.handle.net/2429/32805
Peer Review Status: Reviewed
Scholarly Level: Faculty

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