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

## 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.

Consult NEWS.md for a history of release notes.

## Author(s)

Author and maintainer: Georg M. Goerg <im@gmge.org>

## References

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

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 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 July 1, 2020, 7:50 p.m.