Description Usage Arguments Value See Also Examples
View source: R/foreca.EM.one_weightvector.R
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).
1 2 3 4 5 6 7 8 9 | 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"),
...
)
|
U |
a T \times K array with |
f.U |
multivariate spectrum of class |
spectrum.control |
list; control settings for spectrum estimation.
See |
entropy.control |
list; control settings for entropy estimation.
See |
algorithm.control |
list; control settings for any iterative ForeCA
algorithm. See |
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 |
... |
other arguments passed to |
A list with useful quantities like the optimal weighvector, the corresponding signal, and its forecastability.
foreca.one_weightvector
, foreca.EM-aux
1 2 3 4 5 6 7 | ## 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)
|
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