surrogate.SA: Surrogate Time Series Through Simulated Annealing

In tseriesEntropy: Entropy Based Analysis and Tests for Time Series

Surrogate Time Series Through Simulated Annealing

Description

Starting from a time series x given as input, the function generates surrogate series through Simulated Annealing. Each surrogate series is a constrained random permutation having the same autocorrelation function (up to nlag lags) of the original series x. The surrogates can be used for testing for non linearity in time series.

Usage

surrogate.SA(x, nlag, nsurr, Te = 0.0015, RT = 0.9, eps.SA = 0.05, nsuccmax = 30,
 nmax = 300, che = 1e+05)

Arguments

x	a univariate numeric time series object or a numeric vector.
nlag	minimization is performed w.r.t. to the first nlag lags.
nsurr	number of surrogates.
Te	starting value for the temperature.
RT	reduction factor for the temperature Te.
eps.SA	target tolerance.
nsuccmax	Te is decreased after nsuccmax*N successes.
nmax	Te is decreased after nmax*N successes.
che	after che*2N global iterations the algorithm starts again.

Details

Let N be the length of the series x. Sensible (N-dependent) defaults are derived for the parameters of the algorithm, there should not be the need to change them. In case, the user could try increasing eps.SA.

Value

A list with the following elements:

surr	a matrix with N rows and nsurr columns, in each column is stored a surrogate.
call	contains the call to the routine.

Author(s)

Simone Giannerini<[e-mail]>

References

Giannerini S., Maasoumi E., Bee Dagum E., (2015), Entropy testing for nonlinear serial dependence in time series, Biometrika, 102(3), 661–675 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/biomet/asv007")}.

Schreiber T., Schmitz A.,(2000) Surrogate time series. Physica D, 142(3-4), 346–382.

See Also

See Also Trho.test.SA, surrogate.AR, Trho.test.AR.

Examples

set.seed(1345)
# Generates a AR(1) series
x      <- arima.sim(n=120, model = list(ar=0.8));
x.surr <- surrogate.SA(x, nlag=10, nsurr=3);
plot.ts(x.surr$surr,col=4);


## Check that the surrogates have the same ACF of x
corig <- acf(x,10,plot=FALSE)$acf[,,1];
csurr <- acf(x.surr$surr[,1],10,plot=FALSE)$acf[,,1];
round(cbind(corig,csurr,"abs(difference)"=abs(corig-csurr)),3)

tseriesEntropy documentation built on Aug. 10, 2023, 1:06 a.m.