Neuralnets for Multivariate And Time Series Analysis (NeuMATSA)
MATLAB codes for:
Nonlinear Principal Component Analysis (NLPCA)
Nonlinear Canonical Correlation Analysis (NLCCA) (****
Bug report (2008/1/27) ****
Nonlinear Singular Spectrum Analysis (NLSSA)
The latest release (version 5.0) became available in Oct. 2007.
[The major improvements over the previous version are: (1) The
appropriate weight penalty parameters are now objectively determined by
the codes. (2) Robust options have been introduced in the codes to handle
noisy datasets containing outliers.]
The programs are free software, under the terms of the GNU General Public License
as published by the Free Software Foundation.
The codes are written in
MATLAB and use its
First download the manual:
Hsieh, W.W., 2008. Neuralnets for Multivariate And Time Series
User Manual (in PDF format).
Next download the 2004 general review paper and more recent paper(s) of relevance:
Hsieh, W.W., 2004. Nonlinear multivariate and time series analysis by
neural network methods. Reviews of Geophysics, 42, RG1003,
doi:10.1029/2002RG000112. (reprint with typos corrected in PDF)
Hsieh, W.W., 2007. Nonlinear principal component analysis of
Neural Networks, 20: 434-443. DOI 10.1016/j.neunet.2007.04.018.
(preprint in PDF)
Cannon, A.J. and W.W. Hsieh, 2008.
Robust nonlinear canonical correlation analysis: application to
seasonal climate forecasting.
Nonlinear Processes in Geophysics, 15: 221-232.
(preprint in PDF)
Some of the papers written by our group referenced in this review
paper can be downloaded at the site
Nonlinear Complex Principal Component
Analysis (NLCPCA), Sanjay Rattan (e-mail: "srattan"
followed by "@ualberta.ca") converted the NLPCA
code to complex variables.
There is no written manual other than a file manual.m attached to the
Matlab codes. The relevant publication is:
Rattan, S.S.P. and Hsieh, W.W., 2005. Complex-valued neural networks for
nonlinear complex principal component analysis.
18: 61-69, DOI:10.1016/j.neunet.2004.08.002.
(preprint in PDF).