foreca.EM.one_weightvector: EM-like algorithm to estimate optimal ForeCA transformation

In ForeCA: Forecastable Component Analysis

Description

foreca.EM.one_weightvector finds the optimal weightvector \mathbf{w}^* that gives the most forecastable signal y_t^* = \mathbf{U}_t \mathbf{w}^* using an EM-like algorithm (see References).

Usage

foreca.EM.one_weightvector(
  U,
  f.U = NULL,
  spectrum.control = list(),
  entropy.control = list(),
  algorithm.control = list(),
  init.weightvector = initialize_weightvector(num.series = ncol(U), method = "rnorm"),
  ...
)

Arguments

U	a T \times K array with T observations from the K-dimensional whitened (whiten) time series \mathbf{U}_t. Can be a matrix, data.frame, or a multivariate ts object.
f.U	multivariate spectrum of class 'mvspectrum' with normalize = TRUE.
spectrum.control	list; control settings for spectrum estimation. See complete_spectrum_control for details.
entropy.control	list; control settings for entropy estimation. See complete_entropy_control for details.
algorithm.control	list; control settings for any iterative ForeCA algorithm. See complete_algorithm_control for details.
init.weightvector	numeric; starting point \mathbf{w}_0 for several iterative algorithms. By default it uses a (normalized) random vector from a standard Normal distribution (see initialize_weightvector).
...	other arguments passed to mvspectrum

Value

A list with useful quantities like the optimal weighvector, the corresponding signal, and its forecastability.

See Also

foreca.one_weightvector, foreca.EM-aux

Examples

## Not run: 
XX <- diff(log(EuStockMarkets)[100:200,]) * 100
one.weight <- foreca.EM.one_weightvector(whiten(XX)$U,
                                         spectrum.control =
                                            list(method = "mvspec"))

## End(Not run)

ForeCA documentation built on July 1, 2020, 7:50 p.m.