HR: 0830h
AN: H11F-0910 INVITED     [PDF]
TI: Nonlinear Multivariate and Time Series Analysis by Neural Network Methods, with Applications to ENSO
AU: * Hsieh, W W
EM: whsieh@eos.ubc.ca
AF: Dept. of Earth and Ocean Sciences, University of British Columbia, 6339 Stores Road, Vancouver, BC V6T 1Z4 Canada
AB: 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 (LR) 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 (NLR) and classification. More recently, multi-layer perceptron 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 datasets of the atmosphere and the ocean, especially in the nonlinear study of the El Ni\~no-Southern Oscillation (ENSO) phenomenon.
DE: 3339 Ocean/atmosphere interactions (0312, 4504)
DE: 4231 Equatorial oceanography
DE: 4522 El Ni¤o
SC: Hydrology [H]
MN: 2003 Fall Meeting