ForeCA-package: Implementation of Forecastable Component Analysis (ForeCA)

Description Author(s) References Examples

Description

Forecastable Component Analysis (ForeCA) is a novel dimension reduction technique for multivariate time series \mathbf{X}_t. ForeCA finds a linar combination y_t = \mathbf{X}_t \mathbf{v} that is easy to forecast. The measure of forecastability Ω(y_t) (Omega) is based on the entropy of the spectral density f_y(λ) of y_t: higher entropy means less forecastable, lower entropy is more forecastable.

The main function foreca runs ForeCA on a multivariate time series \mathbf{X}_t.

Please consult the NEWS file for a list of changes to previous versions of this package.

Author(s)

Author and maintainer: Georg M. Goerg <[email protected]>

References

Goerg, G. M. (2013). “Forecastable Component Analysis”. Journal of Machine Learning Research (JMLR) W&CP 28 (2): 64-72, 2013. Available at jmlr.org/proceedings/papers/v28/goerg13.html.

Examples

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XX <- ts(diff(log(EuStockMarkets)))
Omega(XX)

plot(log10(lynx))
Omega(log10(lynx))

## Not run: 
ff <- foreca(XX, n.comp = 4)
ff
plot(ff)
summary(ff)

## End(Not run)

ForeCA documentation built on May 29, 2017, 9:09 a.m.