Empirical Orthogonal Function (EOF) with Spatiotemporal Convertion

version 1.2.0.0 (1.78 KB) by Zhou Chunlüe
Empirical Orthogonal Function (EOF) analysis is often used in Meteorology and Climatology

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Updated 25 Apr 2016

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In statistics and signal processing, the method of empirical orthogonal function (EOF) analysis is a decomposition of a signal or data set in terms of orthogonal basis functions which are determined from the data. It is the same as performing a principal components analysis on the data, except that the EOF method finds both time series and spatial patterns. The term is also interchangeable with the geographically weighted PCAs in geophysics.
if there are too many spatial grids, the spatiotemporal convertion is often performed to quicken the process, other than EOF_analysis.
As required by users, a new version of Empirical Orthogonal Function (EOF) with Spatiotemporal Convertion is provided here.

Cite As

Zhou Chunlüe (2022). Empirical Orthogonal Function (EOF) with Spatiotemporal Convertion (https://www.mathworks.com/matlabcentral/fileexchange/54675-empirical-orthogonal-function-eof-with-spatiotemporal-convertion), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2011b
Compatible with any release
Platform Compatibility
Windows macOS Linux
Acknowledgements

Inspired by: Empirical Orthogonal Function (EOF) analysis

Inspired: EOF

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