Description Author(s) References Examples

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

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.

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

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