gFOBI: Generalized FOBI
In tsBSS: Blind Source Separation and Supervised Dimension Reduction for Time Series

Description Usage Arguments Details Value Author(s) References See Also Examples

The gFOBI (generalized Fourth Order Blind Identification) method for blind source separation of time series with stochastic volatility. The method is a generalization of FOBI, which is a method designed for iid data.

gFOBI(X, ...)

## Default S3 method:
gFOBI(X, k = 0:12, eps = 1e-06, maxiter = 100, method = c("frjd", "rjd"),
      na.action = na.fail, weight = NULL, ordered = FALSE,
      acfk = NULL, original = TRUE, alpha = 0.05, ...)
## S3 method for class 'ts'
gFOBI(X, ...)
## S3 method for class 'xts'
gFOBI(X, ...)
## S3 method for class 'zoo'
gFOBI(X, ...)

`X`	A numeric matrix or a multivariate time series object of class `ts`, `xts` or `zoo`. Missing values are not allowed.
`k`	A vector of lags. It can be any non-negative integer, or a vector consisting of them. Default is `0:12`. If k = 0, this method reduces to `FOBI`.
`eps`	Convergence tolerance.
`maxiter`	The maximum number of iterations.
`method`	The method to use for the joint diagonalization. The options are `"rjd"` and `"frjd"`. Default is `"frjd"`.
`na.action`	A function which indicates what should happen when the data contain 'NA's. Default is to fail.
`weight`	A vector of length k to give weight to the different matrices in joint diagonalization. If NULL, all matrices have equal weight.
`ordered`	Whether to order components according to their volatility. Default is `FALSE`.
`acfk`	A vector of lags to be used in testing the presence of serial autocorrelation. Applicable only if `ordered = TRUE`.
`original`	Whether to return the original components or their residuals based on ARMA fit. Default is `TRUE`, i.e. the original components are returned. Applicable only if `ordered = TRUE`.
`alpha`	Alpha level for linear correlation detection. Default is 0.05.
`...`	Further arguments to be passed to or from methods.

Assume that a p-variate Y with T observations is whitened, i.e. Y = S^(-1/2)*(X_t - (1/T)*sum_t(X_t)), for t = 1, …, T, where S is the sample covariance matrix of X. Algorithm first calculates

B^ij_k(Y) = (1/(T - k))*sum[Y_(t + k) Y_t' E^ij Y_t Y_(t + k)'],

where t = 1, …, T, and then

B_k(Y) = sum(B^ii_k(Y)),

for i = 1, …, p.

The algorithm finds an orthogonal matrix U by maximizing

sum(||diag(U B_k(Y) U')||^2).

The final unmixing matrix is then W = U S^(-1/2).

For ordered = TRUE the function orders the sources according to their volatility. First a possible linear autocorrelation is removed using auto.arima. Then a squared autocorrelation test is performed for the sources (or for their residuals, when linear correlation is present). The sources are then put in a decreasing order according to the value of the test statistic of the squared autocorrelation test. For more information, see lbtest.

A list of class 'bssvol', inheriting from class 'bss', containing the following components:

`W`	The estimated unmixing matrix. If `ordered = TRUE`, the rows are ordered according to the order of the components.
`k`	The vector of the used lags.
`S`	The estimated sources as time series object standardized to have mean 0 and unit variances. If `ordered = TRUE`, then components are ordered according to their volatility. If `original = FALSE`, the sources with linear autocorrelation are replaced by their ARMA residuals.
`MU`	The mean vector of `X`.

If ordered = TRUE, then also the following components included in the list:

`Sraw`	The ordered original estimated sources as time series object standardized to have mean 0 and unit variances. Returned only if `original = FALSE`.
`fits`	The ARMA fits for the components with linear autocorrelation.
`armaeff`	A logical vector. Is TRUE if ARMA fit was done to the corresponding component.
`linTS`	The value of the modified Ljung-Box test statistic for each component.
`linP`	p-value based on the modified Ljung-Box test statistic for each component.
`volTS`	The value of the volatility clustering test statistic.
`volP`	p-value based on the volatility clustering test statistic.

Markus Matilainen, Klaus Nordhausen

Cardoso, J.-F. (1989), Source Separation Using Higher Order Moments, in: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2109–2112.

Matilainen, M., Nordhausen, K. and Oja, H. (2015), New Independent Component Analysis Tools for Time Series, Statistics & Probability Letters, 105, 80–87.

FOBI, frjd, lbtest, auto.arima

if(require("stochvol")) {
n <- 10000
A <- matrix(rnorm(9), 3, 3)

# simulate SV models
s1 <- svsim(n, mu = -10, phi = 0.8, sigma = 0.1)$y
s2 <- svsim(n, mu = -10, phi = 0.9, sigma = 0.2)$y
s3 <- svsim(n, mu = -10, phi = 0.95, sigma = 0.4)$y

X <- cbind(s1, s2, s3) %*% t(A)

res <- gFOBI(X)
res
coef(res)
plot(res)
head(bss.components(res))

MD(res$W, A) # Minimum Distance Index, should be close to zero
}