Title:	Entropy Based Analysis and Tests for Time Series
Version:	0.7-2
Depends:	R (≥ 2.14.0)
Imports:	cubature, methods, parallel, stats, graphics, ks
Suggests:	knitr, rmarkdown
LazyLoad:	yes
Encoding:	UTF-8
Description:	Implements an Entropy measure of dependence based on the Bhattacharya-Hellinger-Matusita distance. Can be used as a (nonlinear) autocorrelation/crosscorrelation function for continuous and categorical time series. The package includes tests for serial and cross dependence and nonlinearity based on it. Some routines have a parallel version that can be used in a multicore/cluster environment. The package makes use of S4 classes.
Maintainer:	Simone Giannerini <simone.giannerini@unibo.it>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
RoxygenNote:	7.2.3
NeedsCompilation:	yes
Packaged:	2023-08-09 15:00:11 UTC; simone.giannerini2
Author:	Simone Giannerini [aut, cre]
Repository:	CRAN
Date/Publication:	2023-08-09 20:40:08 UTC

Entropy Measure Of Serial And Cross Dependence

Description

Entropy based measure of serial and cross dependence for integer or categorical data. Implements a normalized version of the Hellinger/Matusita distance. As shown in the references the metric measure is a proper distance.

Usage

Srho(x, y, lag.max, stationary = TRUE, plot = TRUE, version = c("FORTRAN","R"),
 nor = FALSE)

Arguments

Details

Univariate version: serial entropy

Srho(x, lag.max,
stationary = TRUE, plot = TRUE, version = c("FORTRAN","R"), nor = FALSE)

Bivariate version: cross entropy

Srho(x, y, lag.max,
stationary = TRUE, plot = TRUE, version = c("FORTRAN","R"), nor = FALSE)

This implementation of the measure is normalized to take values in [0, 1]. Normalization is performed with respect to the maximum attainable value computed analytically. This makes the results of Srho comparable among different series.

Value

An object of S4 class "Srho", which is a list with the following elements:

Warning

Unlike ccf the lag k value returned by Srho(x,y) estimates Srho between x[t] and y[t+k]. The result is returned invisibly if plot is TRUE.

Author(s)

Simone Giannerini<simone.giannerini@unibo.it>

References

Granger C. W. J., Maasoumi E., Racine J., (2004) A dependence metric for possibly nonlinear processes. Journal of Time Series Analysis, 25(5), 649–669.

Giannerini S., Maasoumi E., Bee Dagum E., (2015), Entropy testing for nonlinear serial dependence in time series, Biometrika, 102(3), 661–675 doi:10.1093/biomet/asv007.

Maasoumi E., (1993) A compendium to information theory in economics and econometrics. Econometric Reviews, 12(2), 137–181.

See Also

See Also Srho.test. The function Srho.ts implements the same measure for numeric data.

Examples

## UNIVARIATE VERSION
x <- as.integer(rbinom(n=20,size=4,prob=0.5))
Srho(x,lag.max=4)

## BIVARIATE VERSION
y <- as.integer(rbinom(n=20,size=4,prob=0.5))
Srho(x,y,lag.max=4)

## EXAMPLE  1: the effect of normalization
## computes the maximum attainable value by correlating x with itself

set.seed(12)
K    <- 5           # number of categories
smax <- 1-1/sqrt(K) # theoretical maximum under the uniform distribution
x    <- as.integer(sample(1:K,size=1e3,replace=TRUE)) # generates the sequence
S    <- Srho(x,x,lag.max=2,nor=FALSE,plot=FALSE)

plot(S,lwd=2,col=4)
abline(h=smax,col=2,lty=2)
text(x=-1,y=0.54,labels=paste("theoretical maximum = ",round(smax,4),sep=""),col=2)
text(x=-1,y=0.45,labels=paste("estimated maximum = ",round(S[3],4),sep=""),col=4)

Class "Srho"

Description

A class for Srho and its extensions

Objects from the Class

Objects can be created by calls of the form new("Srho", ...).

Slots

.Data:

Object of class "numeric": contains Srho computed on the data set.

lags:

Object of class "integer": contains the lags at which Srho is computed.

stationary:

Object of class "logical": TRUE if the stationary version is computed.

data.type:

Object of class "character": contains the data type.

notes:

Object of class "character": additional notes.

Methods

plot

signature(x = "Srho", y = "missing"): ...

show

signature(object = "Srho"): ...

Author(s)

Simone Giannerini <simone.giannerini@unibo.it>

See Also

See Also Srho.test

Examples

showClass("Srho")

Entropy Test For Serial And Cross Dependence For Categorical Sequences

Description

Bootstrap/permutation tests of serial and cross dependence for integer or categorical sequences.

Usage

Srho.test(x, y, lag.max=10, B = 1000, stationary = TRUE, plot = TRUE,
quant = c(0.95, 0.99), nor = FALSE)

Arguments

Details

Univariate version: test for serial dependence

Srho.test(x, lag.max, B = 1000,
stationary = TRUE, plot = TRUE, quant = c(0.95, 0.99), nor = FALSE)

Bivariate version: test for cross dependence

Srho.test(x, y, lag.max, B = 1000,
stationary = TRUE, plot = TRUE, quant = c(0.95, 0.99), nor = FALSE)

Value

An object of class "Srho.test", which is a list with the following elements:

Warning

Unlike ccf the lag k value returned by Srho.test(x,y) estimates Srho between x[t] and y[t+k]. The result is returned invisibly if plot is TRUE.

Author(s)

Simone Giannerini<simone.giannerini@unibo.it>

References

Granger C. W. J., Maasoumi E., Racine J., (2004) A dependence metric for possibly nonlinear processes. Journal of Time Series Analysis, 25(5), 649–669.

Maasoumi E., (1993) A compendium to information theory in economics and econometrics. Econometric Reviews, 12(2), 137–181.

See Also

See also Srho, Srho.ts. The function Srho.test.ts implements the same test for numeric data.

Examples

set.seed(12)
x <- as.integer(rbinom(n=30,size=4,prob=0.5))
y <- as.integer(rbinom(n=30,size=4,prob=0.5))
z <- as.integer(c(4,abs(x[-30]*2-2))-rbinom(n=30,size=1,prob=1/2))

# no dependence
Srho.test(x,lag.max=4)   # univariate
Srho.test(x,y,lag.max=4) # bivariate

# lag 1 dependence
Srho.test(x,z,lag.max=4) # bivariate

Class "Srho.test"

Description

A class of tests for serial dependence and nonlinearity based upon Srho.

Objects from the Class

Objects can be created by calls of the form new("Srho.test", ...).

Slots

.Data:

Object of class "numeric": contains Srho computed on the data set.

call:

Object of class "call": contains the call to the routine.

call.h:

Object of class "call": contains the call to the routine used for obtaining the surrogates or the bootstrap replicates under the null hypothesis.

quantiles:

Object of class "matrix": contains the quantiles of the bootstrap/permutation distribution under the null hypothesis.

test.type:

Object of class "character": contains a description of the type of test performed.

significant.lags:

Object of class "list": contains the lags at which Srho exceeds the confidence bands at quant under the null hypothesis.

p.value:

Object of class "numeric": contains the bootstrap p-value for each lag.

lags:

Object of class "integer": contains the lags at which Srho is computed.

stationary:

TRUE if the stationary version is computed.

data.type:

Object of class "character": contains the data type.

notes:

Object of class "character": additional notes.

Extends

Class "Srho", directly.

Methods

plot

signature(x = "Srho.test", y = "missing"): ...

show

signature(object = "Srho.test"): ...

Author(s)

Simone Giannerini <simone.giannerini@unibo.it>

See Also

See Also Srho

Examples

showClass("Srho.test")

Entropy Tests For Nonlinearity In Time Series - Parallel Version

Description

Entropy test of nonlinearity for time series based on Srho.ts and surrogate data obtained through the sieve bootstrap. The parallel version requires parallel.

Usage

Srho.test.AR(x, y, lag.max = 10, B = 100, plot = TRUE, quant = c(0.95, 0.99),
 bw = c("reference", "mlcv", "lscv", "scv", "pi"), bdiag=TRUE,
 method = c("integral", "summation"), tol = 0.001, order.max = NULL,
 fit.method=c("yule-walker", "burg", "ols", "mle", "yw"),  smoothed = TRUE ,...)

## Parallel version

Srho.test.AR.p(x, y, lag.max = 10, B = 100, plot = TRUE, quant = c(0.95, 0.99),
 bw = c("reference", "mlcv", "lscv", "scv", "pi"), bdiag=TRUE,
 method = c("integral", "summation"), tol = 0.001, order.max = NULL,
 fit.method=c("yule-walker", "burg", "ols", "mle", "yw"),  smoothed = TRUE,
 nwork=detectCores(),...)

Arguments

Details

For each lag from 1 to lag.max Srho.test.AR computes a test for nonlinearity for time series based on Srho.ts. The distribution under the null hypothesis of linearity is obtained through the sieve bootstrap. The routine requires the package parallel to spawn multiple workers.

Value

An object of class "Srho.test", which is a list with the following elements:

Author(s)

Simone Giannerini<simone.giannerini@unibo.it>

References

Giannerini S., Maasoumi E., Bee Dagum E., (2015), Entropy testing for nonlinear serial dependence in time series, Biometrika, 102(3), 661–675 doi:10.1093/biomet/asv007.

See Also

See Also Srho.ts, surrogate.AR, surrogate.ARs, Srho.test.AR.

Examples

## Not run: 
## ************************************************************
## WARNING: computationally intensive, increase B with caution
## ************************************************************

# modify nwork to match the number of available cores
set.seed(13)
x      <- arima.sim(n=120, model = list(ar=0.8));
result <- Srho.test.AR.p(x, lag.max = 5,  B = 100, bw='reference', method='integral', nwork=2)

## ** Compare timings **
system.time(Srho.test.AR.p(x, lag.max = 5,  B = 100, bw='reference', method='integral', nwork=4))
system.time(Srho.test.AR(x, lag.max = 5,  B = 100, bw='reference', method='integral'))

## End(Not run)

Entropy Tests Of Serial And Cross Dependence For Time Series

Description

Entropy test of serial and cross dependence for numeric time series (continuous state space) based on Srho.ts. The distribution under the null hypothesis of independence is obtained by means of bootstrap/permutations methods (see ci.type). The parallel version requires parallel.

Usage

Srho.test.ts(x, y, lag.max = 10,  B = 100, plot = TRUE, quant = c(0.95, 0.99),
 bw = c("reference","mlcv", "lscv", "scv", "pi"), bdiag=TRUE,
 method =c("integral","summation"), tol=1e-03, ci.type = c("mbb","perm"),...)

## Parallel version  
Srho.test.ts.p(x, y, lag.max = 10,  B = 100, plot = TRUE, quant = c(0.95, 0.99),
 bw = c("reference","mlcv", "lscv", "scv", "pi"), bdiag=TRUE,
 method =c("integral","summation"), tol=1e-03, ci.type = c("mbb","perm"), 
 nwork=detectCores(),...)

Arguments

Details

Univariate version: test for serial dependence

Srho.test.ts.p(x, lag.max = 10,
B = 100, plot = TRUE, quant = c(0.95, 0.99), bdiag=TRUE,
bw = c("reference", "mlcv", "lscv", "scv", "pi"), method =c("integral","summation"), 
tol=1e-03, ci.type = c("perm"), nwork=detectCores())

Bivariate version: test for cross dependence

Srho.test.ts.p(x, y, lag.max = 10,
B = 100, plot = TRUE, quant = c(0.95, 0.99), bdiag=TRUE, 
bw = c("reference", "mlcv", "lscv", "scv", "pi"), method =c("integral","summation"), 
tol=1e-03, ci.type = c("mbb","perm"), nwork=detectCores())

For each lag from 1 to lag.max (serial dependence) or from -lag.max to lag.max (cross dependence) Srho.test.ts computes a test for serial/cross dependence for time series based on Srho.ts. The distribution under the null hypothesis of independence is obtained through either permutation or bootstrap methods. If the option mbb is chosen (bivariate case only) the resampled series use a moving block bootstrap to acccount for the serial dependence of the original series so that the test will have better size than the permutation version.

Value

An object of class "Srho.test", which is a list with the following elements:

Author(s)

Simone Giannerini<simone.giannerini@unibo.it>

References

Granger C. W. J., Maasoumi E., Racine J., (2004) A dependence metric for possibly nonlinear processes. Journal of Time Series Analysis, 25(5), 649–669.

Maasoumi E., (1993) A compendium to information theory in economics and econometrics. Econometric Reviews, 12(2), 137–181.

See Also

See Also Srho.test.ts and Srho.ts. The function Srho.test implements the same test for integer/categorical data. For a test for nonlinear serial dependence see Srho.test.AR, Trho.test.AR, Trho.test.SA, together with their parallel versions: Srho.test.AR.p, Trho.test.AR, Trho.test.SA.

Examples

## Not run: 
## ************************************************************
## WARNING: computationally intensive, increase B with caution
## ************************************************************
set.seed(13)
n      <- 120
w      <- rnorm(n)
x      <- arima.sim(n, model = list(ar=0.8));
y      <- arima.sim(n, model = list(ar=0.8));
z      <- lag(x,-1) + rnorm(n,sd=2) # dependence at lag 1

# UNIVARIATE VERSION
res1 <- Srho.test.ts.p(w, lag.max = 5,  B = 40, ci.type="perm") # independence
res2 <- Srho.test.ts.p(x, lag.max = 5,  B = 40, ci.type="perm") # dependence

# BIVARIATE VERSION
res3 <- Srho.test.ts.p(x, y, lag.max = 5,  B = 40, ci.type="mbb") # independence
res4 <- Srho.test.ts.p(x, z, lag.max = 5,  B = 40, ci.type="mbb") # dependence

## End(Not run)

Entropy Measure Of Serial And Cross Dependence

Description

Entropy based measure of serial and cross dependence for continuous data. For integer/categorical data see Srho. Implements a normalized version of the Hellinger/Matusita distance. As shown in the references the metric measure is a proper distance.

Usage

Srho.ts(x, y, lag.max = 10, bw = c("reference", "mlcv", "lscv", "scv", "pi"),
bdiag=TRUE, method = c("integral", "summation"), plot = TRUE, tol = 0.001, ...)

Arguments

Details

Univariate version: serial entropy

Srho.ts(x, lag.max = 10,
 bw = c("reference", "mlcv", "lscv", "scv", "pi"), bdiag=TRUE,
 method = c("integral", "summation"), plot = TRUE, tol = 0.001)

Bivariate version: cross entropy

Srho.ts(x, y, lag.max = 10,
 bw = c("reference", "mlcv", "lscv", "scv", "pi"), bdiag=TRUE,
 method = c("integral", "summation"), plot = TRUE, tol = 0.001)

The bandwidth selection methods are the following:

reference:

reference criterion.

mlcv:

maximum likelihood cross-validation.

lscv:

least-squares cross-validation, see Hlscv.

scv:

smoothed cross-validation, see Hscv

pi:

plugin, see Hpi

If bdiag = TRUE (the default), the diagonal bandwidth selectors Hlscv.diag, Hscv.diag, Hpi.diag are used.

Value

An object of class "Srho.ts", with the following slots:

Author(s)

Simone Giannerini<simone.giannerini@unibo.it>

References

Granger C. W. J., Maasoumi E., Racine J., (2004) A dependence metric for possibly nonlinear processes. Journal of Time Series Analysis, 25(5), 649–669.

Maasoumi E., (1993) A compendium to information theory in economics and econometrics. Econometric Reviews, 12(2), 137–181.

Giannerini S., Maasoumi E., Bee Dagum E., (2015), Entropy testing for nonlinear serial dependence in time series, Biometrika, 102(3), 661–675 doi:10.1093/biomet/asv007.

See Also

Srho.test.ts, hcubature, ks. The function Srho implements the same measure for integer/categorical data.

Examples

set.seed(11)
x <- arima.sim(list(order = c(1,0,0), ar = 0.8), n = 50)
S <- Srho.ts(x,lag.max=5,method="integral",bw="mlcv")

# creates a nonlinear dependence at lag 1
y <- c(runif(1),x[-50]^2*0.8-0.3)
S <- Srho.ts(x,y,lag.max=3,method="integral",bw="mlcv")

Class "Srho.ts"

Description

A class for Srho for continuous data/time series.

Objects from the Class

Objects can be created by calls of the form new("Srho.ts", ...).

Slots

.Data:

Object of class "numeric": contains Srho computed on the data set.

method:

Object of class "character": computation method, can be "integral" or "summation".

bandwidth:

Object of class "character": bandwidth selection method.

lags:

Object of class "integer": contains the lags at which Srho is computed.

stationary:

Object of class "logical": TRUE if the stationary version is computed.

data.type:

Object of class "character": contains the data type.

notes:

Object of class "character": additional notes.

Extends

Class "Srho", directly.

Methods

show

signature(object = "Srho.ts"): ...

Author(s)

Simone Giannerini<simone.giannerini@unibo.it>

See Also

See Also Srho.test, Srho

Examples

showClass("Srho.ts")

Entropy Tests For Nonlinearity In Time Series - Parallel Version

Description

Entropy test of nonlinearity for time series based on Srho.ts and surrogate data obtained through the sieve bootstrap (AR modeling). The parallel version requires parallel.

Usage

Trho.test.AR(x, y, lag.max = 10, B = 100, plot = TRUE, quant = c(0.95, 0.99),
 bw = c("reference", "mlcv", "lscv", "scv", "pi"), bdiag=TRUE,
 method = c("integral", "summation"), tol = 0.001, order.max = NULL,
 fit.method=c("yule-walker", "burg", "ols", "mle", "yw"), smoothed = TRUE,...)

## Parallel version

Trho.test.AR.p(x, y, lag.max = 10, B = 100, plot = TRUE, quant = c(0.95, 0.99),
 bw = c("reference", "mlcv", "lscv", "scv", "pi"), bdiag=TRUE,
 method = c("integral", "summation"), tol = 0.001, order.max = NULL,
 fit.method=c("yule-walker", "burg", "ols", "mle", "yw"),  smoothed = TRUE,
 nwork=detectCores(),...)

Arguments

Details

For each lag from 1 to lag.max Trho.test.AR computes a test for nonlinearity for time series based on Srho.ts. The distribution under the null hypothesis of a linear Gaussian process is obtained through the sieve bootstrap. The routine requires the package parallel to spawn multiple workers.

Value

An object of class "Srho.test", which is a list with the following elements:

Author(s)

Simone Giannerini<simone.giannerini@unibo.it>

References

Giannerini S., Maasoumi E., Bee Dagum E., (2015), Entropy testing for nonlinear serial dependence in time series, Biometrika, 102(3), 661–675 doi:10.1093/biomet/asv007.

See Also

See Also Srho.ts, surrogate.AR, surrogate.ARs, Trho.test.AR.

Examples

## Not run: 

# modify nwork to match the number of available cores
set.seed(13)
b      <- 100
x      <- arima.sim(n=120, model = list(ar=0.8));
result <- Trho.test.AR.p(x, lag.max = 5, B=b, nwork=2)

## ** Compare timings **
system.time(Trho.test.AR.p(x,lag.max = 5,B=b, nwork=4))
system.time(Trho.test.AR(x,  lag.max = 5,B=b))

## End(Not run)

Entropy Tests For Nonlinearity In Time Series - Parallel Version

Description

Entropy test of nonlinearity for time series based on Srho.ts and surrogate data obtained through Simulated Annealing. The parallel version requires parallel.

Usage

Trho.test.SA(x, y, lag.max = 10,  B = 100, plot = TRUE, quant = c(0.95, 0.99),
 bw = c("reference","mlcv", "lscv", "scv", "pi"), bdiag=TRUE,
 method =c("integral","summation"), tol=1e-03, nlag=trunc(length(x)/4),
 Te=0.0015, RT=0.9, eps.SA=0.05, nsuccmax=30, nmax=300, che=100000,...) 
 
Trho.test.SA.p(x, y, lag.max = 10,  B = 100, plot = TRUE, quant = c(0.95, 0.99),
 bw = c("reference","mlcv", "lscv", "scv", "pi"), bdiag=TRUE,
  method =c("integral","summation"), tol=1e-03, nlag=trunc(length(x)/4), Te=0.0015,
 RT=0.9, eps.SA=0.05, nsuccmax=30, nmax=300, che=100000, nwork=detectCores(),...)

Arguments

Details

For each lag from 1 to lag.max Trho.test.SA computes a test for nonlinearity for time series based on Srho.ts. The distribution under the null hypothesis of a linear Gaussian process is obtained through a generalization of surrogate data methods. Surrogate time series are obtained through Simulated Annealing (SA). Sensible (N-dependent) defaults are derived for the parameters of the SA algorithm, there should not be the need to change them. The routine requires the package parallel to spawn multiple workers.

Value

An object of class "Srho.test", which is a list with the following elements:

Author(s)

Simone Giannerini<simone.giannerini@unibo.it>

References

Giannerini S., Maasoumi E., Bee Dagum E., (2015), Entropy testing for nonlinear serial dependence in time series, Biometrika, 102(3), 661–675 doi:10.1093/biomet/asv007.

See Also

See Also Srho.ts, surrogate.SA, Trho.test.SA.

Examples

## Not run: 
# modify nwork to match the number of available cores
set.seed(13)
x      <- arima.sim(n=120, model = list(ar=0.8));
result <- Trho.test.SA.p(x, lag.max = 5, B = 100, bw='reference', nwork=2)

## ** Compare timings **
system.time(Trho.test.SA.p(x, lag.max = 5, B = 100, bw='reference', nwork=4))
system.time(Trho.test.SA(x, lag.max = 5, B = 100, bw='reference'))

## End(Not run)

Surrogate Time Series Through AR Modeling (Sieve Bootstrap)

Description

Starting from a time series x given as input, the function generates surrogate series by means of the sieve bootstrap. The surrogates can be used for testing for non linearity in time series.

Usage

surrogate.AR(x, order.max = NULL, fit.method = c("yule-walker",
 "burg", "ols", "mle", "yw"), nsurr) 

Arguments

.

Details

Let N be the length of the series x. The best AR model is chosen by means of the AIC criterion. The residuals of the model are resampled with replacement. Surrogate series are obtained by driving the fitted model with the resampled residuals.

Value

A list with the following elements:

Author(s)

Simone Giannerini<simone.giannerini@unibo.it>

References

Giannerini S., Maasoumi E., Bee Dagum E., (2015), Entropy testing for nonlinear serial dependence in time series, Biometrika, 102(3), 661–675 doi:10.1093/biomet/asv007.

Buhlmann, P., (1997). Sieve bootstrap for time series. Bernoulli, 3, 123–148.

See Also

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

Examples

set.seed(1345)
# Generates a AR(1) series
x      <- arima.sim(n=120, model = list(ar=0.8));
x.surr <- surrogate.AR(x, 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)

Surrogate Time Series Through A Modeling (Smoothed Sieve Bootstrap)

Description

Starting from a time series x given as input, the function generates surrogate series by means of the smoothed sieve bootstrap. The surrogates can be used for testing for non linearity in time series.

Usage

surrogate.ARs(x, order.max = NULL,
 fit.method = c("yule-walker","burg", "ols", "mle", "yw"), nsurr)

Arguments

Details

Let N be the length of the series x. The best AR model is chosen by means of the AIC criterion. Surrogate series are obtained by driving the fitted model with the smoothed resampled residuals. Smoothing is performed through Kernel density estimation with a Gaussian Kernel by using the dafaults of density.

Value

A list with the following elements:

Author(s)

Simone Giannerini<simone.giannerini@unibo.it>

References

Giannerini S., Maasoumi E., Bee Dagum E., (2015), Entropy testing for nonlinear serial dependence in time series, Biometrika, 102(3), 661–675 doi:10.1093/biomet/asv007.

Bickel, P., Buhlmann, P., (1999). A new mixing notion and functional central limit theorems for a sieve bootstrap in time series. Bernoulli 5, 413–446.

See Also

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

Examples

set.seed(1345)
# Generates a AR(1) series
x      <- arima.sim(n=120, model = list(ar=0.8));
x.surr <- surrogate.ARs(x, order.max=NULL, 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)

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

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:

Author(s)

Simone Giannerini<simone.giannerini@unibo.it>

References

Giannerini S., Maasoumi E., Bee Dagum E., (2015), Entropy testing for nonlinear serial dependence in time series, Biometrika, 102(3), 661–675 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)