Forecastable Component Analysis
List of common arguments
Completes several control settings
Shannon entropy for a continuous pdf
Shannon entropy for discrete pmf
Implementation of Forecastable Component Analysis (ForeCA)
Plot, summary, and print methods for class 'foreca'
ForeCA EM auxiliary functions
EM-like algorithm to estimate optimal ForeCA transformation
Plot, summary, and print methods for class 'foreca.one_weightvector'
Forecastable Component Analysis
Initialize weightvector for iterative ForeCA algorithms
S3 methods for class 'mvspectrum'
Estimates spectrum of multivariate time series
Compute (weighted) covariance matrix from frequency spectrum
Estimate forecastability of a time series
Computes quadratic form x' A x
Slow Feature Analysis
Estimates spectral entropy of a time series
whitens multivariate data
Implementation of Forecastable Component Analysis ('ForeCA'), including main algorithms and auxiliary function (summary, plotting, etc.) to apply 'ForeCA' to multivariate time series data. 'ForeCA' is a novel dimension reduction (DR) technique for temporally dependent signals. Contrary to other popular DR methods, such as 'PCA' or 'ICA', 'ForeCA' takes time dependency explicitly into account and searches for the most ''forecastable'' signal. The measure of forecastability is based on the Shannon entropy of the spectral density of the transformed signal.