This project provides a Fortran90 library and a python module for singular spectrum analyses such as PCA/EOF or MSSA. It is intended for people interested, for example, in analysing climate or financial variability.
- PCA for extracting dominant spatial pattern of variability.
- SSA/MSSA which is like PCA but for extracting spatio-temporal patterns.
- Joined SVD which is like PCA but for extracting the common variability of two variables (using cross-covariances).
- SVD model deduce a predictand from a predictor using SVD.
- Full missing value support.
- Switch of space and time for PCA analyses performed on a large number of channels.
- For analysis, empirical orthogonal functions (EOFs), principal componants (PCs), expansion coefficient (ECs) and reconstructions of signal are available.
- Analyses can be performed on several variables at the same time, with normalisation coefficents [python].
- Pre-PCA analysis to reduct the d-o-f before analysing huge datasets with MSSA or SVD.
- Monte-Carlo test for (M)SSA (MC-SSA) [python].
- Full UV-CDAT support [python].
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