varima.sim: Simulate Data From Seasonal/Nonseasonal...
In portes: Portmanteau Tests for Time Series Models
varima.sim R Documentation
Simulate Data From Seasonal/Nonseasonal ARIMA(p,d,q)*(ps,ds,qs)_s or VARIMA(p,d,q)*(ps,ds,qs)_s Models
Description
Simulate time series from AutoRegressive Integrated Moving Average, ARIMA(p,d,q), or
Vector Integrated AutoRegressive Moving Average, VARIMA(p,d,q),
where d is a nonnegative difference integer in the ARIMA case
and it is a vector of k differenced components
d_1,...,d_k in the VARIMA case.
In general, this function can be implemented in simulating univariate or multivariate
Seasonal AutoRegressive Integrated Moving Average, SARIMA(p,d,q)*(ps,ds,qs)_s
and SVARIMA(p,d,q)*(ps,ds,qs)_s, where ps and qs are
the orders of the seasonal univariate/multivariate AutoRegressive and Moving Average components respectively.
ds is a nonnegative difference integer in the SARIMA case
and it is a vector of k differenced components ds_1,...,ds_k in the SVARIMA case,
whereas s is the seasonal period.
The simulated process may have a deterministic terms, drift constant and time trend, with non-zero mean.
The innovations may have finite or infinite variance.
Usage
varima.sim(model=list(ar=NULL,ma=NULL,d=NULL,sar=NULL,sma=NULL,D=NULL,period=NULL),
n,k=1,constant=NA,trend=NA,demean=NA,innov=NULL,
innov.dist=c("Gaussian","t","bootstrap"),...)
Arguments
model
a list with univariate/multivariate component ar and/or ma and/or sar
and/or sma giving the univariate/multivariate AR and/or MA and/or SAR
and/or SMA coefficients respectively.
period specifies the seasonal period.
For seasonality, default is NULL indicates that period =12.
d and D are integer or vector representing the order of the usual and seasonal difference.
An empty list gives an ARIMA(0, 0, 0)*(0,0,0)_null model, that is white noise.
n
length of the series.
k
number of simulated series. For example, k=1 is used for univariate series and k=2 is used for bivariate series.
constant
a numeric vector represents the intercept in the deterministic equation.
trend
a numeric vector represents the slop in the deterministic equation.
demean
a numeric vector represents the mean of the series.
innov
a vector of univariate or multivariate innovation series.
This may used as an initial series to genrate innovations with innov.dist = "bootstrap".
This argument is irrelevant with the other selections of innov.dist.
innov.dist
distribution to generate univariate or multivariate innovation process.
This could be Gaussian, t, or bootstrap using resampled errors rather than distributed errors.
Default is Gaussian.
...
arguments to be passed to methods, such as dft degrees of freedom
needed to generate innovations with univariate/multivariate series with t-distribution innovations.
The argument trunc.lag represents the truncation lag that is used
to truncate the infinite MA or VMA Process.
IF it is not given, then trunc.lag = min(100, n/3).
Optionally sigma is the variance of a Gaussian or t white noise series.
Details
This function is used to simulate a univariate/multivariate seasonal/nonseasonal
SARIMA or SVARIMA model of order (p,d,q)\times(ps,ds,qs)_s
\phi(B)\Phi(B^s)d(B)D(B^s)(Z_{t})-\mu = a + b \times t + \theta(B)\Theta(B^s)e_{t},
where a, b, and \mu correspond to the arguments constant, trend, and demean respectively.
The univariate or multivariate series e_{t} represents the innovations series given from the argument innov.
If innov = NULL then e_{t} will be generated from
a univariate or multivariate normal distribution or t-distribution.
\phi(B) and \theta(B) are the VAR and the VMA coefficient matrices respectively
and B is the backshift time operator.
\Phi(B^s) and \Theta(B^s) are the Vector SAR Vector SMA
coefficient matrices respectively.
d(B)=diag[(1-B)^{d_{1}},\ldots,(1-B)^{d_{k}}] and
D(B^s)=diag[(1-B^s)^{ds_{1}},\ldots,(1-B^s)^{ds_{k}}] are diagonal matrices.
This states that each individual series Z_{i}, i=1,...,k is differenced d_{i}ds_{i} times
to reduce to a stationary Vector ARMA(p,0,q)*(ps,0,qs)_s series.
Value
Simulated data from SARIMA(p,d,q) or SVARIMA(p,d,q)*(ps,ds,qs)_s process
that may have a drift and deterministic time trend terms.
Author(s)
Esam Mahdi and A.I. McLeod.
References
Hipel, K.W. and McLeod, A.I. (2005). "Time Series Modelling of Water Resources and Environmental Systems".
Reinsel, G. C. (1997). "Elements of Multivariate Time Series Analysis". Springer-Verlag, 2nd edition.
See Also
arima.sim, vma.sim, ImpulseVMA, InvertQ
Examples
#################################################################################
# Simulate white noise series from a Gaussian distribution #
#################################################################################
set.seed(1234)
Z1 <- varima.sim(n=400) ## a univariate series
plot(Z1)
Z2 <- varima.sim(n=400,k=2) ## a bivariate series
plot(Z2)
Z3 <- varima.sim(n=400,k=5) ## a multivariate series of dimension 5
plot(Z3)
#################################################################################
# Simulate MA(1) where innovation series is provided via argument innov #
#################################################################################
set.seed(1234)
n <- 200
ma <- 0.6
Z<-varima.sim(list(ma=ma),n=n,innov=rnorm(n),innov.dist="bootstrap")
plot(Z)
#################################################################################
# Simulate seasonal ARIMA(2,1,0)*(0,2,1)_12 process with ar=c(1.3,-0.35), #
# ma.season = 0.8. Gaussian innovations. The series is truncated at lag 50 #
#################################################################################
set.seed(12834)
n <- 100
ar <- c(1.3, -0.35)
ma.season <- 0.8
Z<-varima.sim(list(ar=ar,d=1,sma=ma.season,D=2),n=n,trunc.lag=50)
plot(Z)
acf(Z)
#################################################################################
# Simulate seasonal ARMA(0,0,0)*(2,0,0)_4 process with intercept = 0.8 #
# ar.season = c(0.9,-0.9), period = 4, t5-distribution innovations with df = 3 #
#################################################################################
set.seed(1234)
n <- 200
ar.season <- c(0.9,-0.9)
Z<-varima.sim(list(sar=ar.season,period=4),n=n,constant=0.8,innov.dist="t",dft=3)
plot(Z)
acf(Z)
arima(Z,order=c(0,0,0),seasonal = list(order = c(2,0,0),period=4))
#################################################################################
# Simulate a bivariate white noise series from a multivariate t4-distribution #
# Then use the nonparametric bootstrap method to generate a seasonal SVARIMA #
# of order (0,d,0)*(0,0,1)_12 with d = c(1, 0), n= 250, k = 2, and #
# ma.season=array(c(0.5,0.4,0.1,0.3),dim=c(k,k,1)) #
#################################################################################
set.seed(1234)
Z1 <- varima.sim(n=250,k=2,innov.dist="t",dft=4)
ma.season=array(c(0.5,0.4,0.1,0.3),dim=c(2,2,1))
Z2 <- varima.sim(list(sma=ma.season,d=c(1,0)),n=250,k=2,
innov=Z1,innov.dist="bootstrap")
plot(Z2)
#################################################################################
# Simulate a bivariate VARIMA(2,d,1) process with length 300, where d=(1,2). #
# ar = array(c(0.5,0.4,0.1,0.5,0,0.3,0,0),dim=c(k,k,2)), #
# ma = array(c(0,0.25,0,0), dim=c(k,k,1)). #
# innovations are generated from multivariate normal #
# The process have mean zero and no deterministic terms. #
# The variance covariance is sigma = matrix(c(1,0.71,0.71,2),2,2). #
# The series is truncated at default value: trunc.lag=ceiling(100/3)=34 #
#################################################################################
set.seed(1234)
k <- 2
n <- 300
ar <- array(c(0.5,0.4,0.1,0.5,0,0.3,0,0),dim=c(k,k,2))
ma <- array(c(0,0.25,0,0),dim=c(k,k,1))
d <- c(1,2)
sigma <- matrix(c(1,0.71,0.71,2),k,k)
Z <- varima.sim(list(ma=ar,ma=ma,d=d),n=n,k=2,sigma=sigma)
plot(Z)
#################################################################################
# Simulate a trivariate Vector SVARMA(1,0,0)*(1,0,0)_12 process with length 300 #
# ar = array(c(0.5,0.4,0.1,0.5,0,0.3,0,0,0.1), dim=c(k,k,1)), where k =3 #
# ar.season = array(c(0,0.25,0,0.5,0.1,0.4,0,0.25,0.6), dim=c(k,k,1)). #
# innovations are generated from multivariate normal distribution #
# The process have mean c(10, 0, 12), #
# Drift equation a + b * t, where a = c(2,1,5), and b = c(0.01,0.06,0) #
# The series is truncated at default value: trunc.lag=ceiling(100/3)=34 #
#################################################################################
set.seed(1234)
k <- 3
n <- 300
ar <- array(c(0.5,0.4,0.1,0.5,0,0.3,0,0,0.1),dim=c(k,k,1))
ar.season <- array(c(0,0.25,0,0.5,0.1,0.4,0,0.25,0.6),dim=c(k,k,1))
constant <- c(2,1,5)
trend <- c(0.01,0.06,0)
demean <- c(10,0,12)
Z <- varima.sim(list(ar=ar,sar=ar.season),n=n,k=3,constant=constant,
trend=trend,demean=demean)
plot(Z)
acf(Z)
portes documentation built on July 9, 2023, 5:07 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| varima.sim | R Documentation |
Simulate Data From Seasonal/Nonseasonal ARIMA(p,d,q)*(ps,ds,qs)_s or VARIMA(p,d,q)*(ps,ds,qs)_s Models
Description
Simulate time series from AutoRegressive Integrated Moving Average, ARIMA(p,d,q), or
Vector Integrated AutoRegressive Moving Average, VARIMA(p,d,q),
where d is a nonnegative difference integer in the ARIMA case
and it is a vector of k differenced components
d_1,...,d_k in the VARIMA case.
In general, this function can be implemented in simulating univariate or multivariate
Seasonal AutoRegressive Integrated Moving Average, SARIMA(p,d,q)*(ps,ds,qs)_s
and SVARIMA(p,d,q)*(ps,ds,qs)_s, where ps and qs are
the orders of the seasonal univariate/multivariate AutoRegressive and Moving Average components respectively.
ds is a nonnegative difference integer in the SARIMA case
and it is a vector of k differenced components ds_1,...,ds_k in the SVARIMA case,
whereas s is the seasonal period.
The simulated process may have a deterministic terms, drift constant and time trend, with non-zero mean.
The innovations may have finite or infinite variance.
Usage
varima.sim(model=list(ar=NULL,ma=NULL,d=NULL,sar=NULL,sma=NULL,D=NULL,period=NULL),
n,k=1,constant=NA,trend=NA,demean=NA,innov=NULL,
innov.dist=c("Gaussian","t","bootstrap"),...)
Arguments
model |
a list with univariate/multivariate component |
n |
length of the series. |
k |
number of simulated series. For example, |
constant |
a numeric vector represents the intercept in the deterministic equation. |
trend |
a numeric vector represents the slop in the deterministic equation. |
demean |
a numeric vector represents the mean of the series. |
innov |
a vector of univariate or multivariate innovation series.
This may used as an initial series to genrate innovations with |
innov.dist |
distribution to generate univariate or multivariate innovation process.
This could be |
... |
arguments to be passed to methods, such as |
Details
This function is used to simulate a univariate/multivariate seasonal/nonseasonal
SARIMA or SVARIMA model of order (p,d,q)\times(ps,ds,qs)_s
\phi(B)\Phi(B^s)d(B)D(B^s)(Z_{t})-\mu = a + b \times t + \theta(B)\Theta(B^s)e_{t},
where a, b, and \mu correspond to the arguments constant, trend, and demean respectively.
The univariate or multivariate series e_{t} represents the innovations series given from the argument innov.
If innov = NULL then e_{t} will be generated from
a univariate or multivariate normal distribution or t-distribution.
\phi(B) and \theta(B) are the VAR and the VMA coefficient matrices respectively
and B is the backshift time operator.
\Phi(B^s) and \Theta(B^s) are the Vector SAR Vector SMA
coefficient matrices respectively.
d(B)=diag[(1-B)^{d_{1}},\ldots,(1-B)^{d_{k}}] and
D(B^s)=diag[(1-B^s)^{ds_{1}},\ldots,(1-B^s)^{ds_{k}}] are diagonal matrices.
This states that each individual series Z_{i}, i=1,...,k is differenced d_{i}ds_{i} times
to reduce to a stationary Vector ARMA(p,0,q)*(ps,0,qs)_s series.
Value
Simulated data from SARIMA(p,d,q) or SVARIMA(p,d,q)*(ps,ds,qs)_s process
that may have a drift and deterministic time trend terms.
Author(s)
Esam Mahdi and A.I. McLeod.
References
Hipel, K.W. and McLeod, A.I. (2005). "Time Series Modelling of Water Resources and Environmental Systems".
Reinsel, G. C. (1997). "Elements of Multivariate Time Series Analysis". Springer-Verlag, 2nd edition.
See Also
arima.sim, vma.sim, ImpulseVMA, InvertQ
Examples
#################################################################################
# Simulate white noise series from a Gaussian distribution #
#################################################################################
set.seed(1234)
Z1 <- varima.sim(n=400) ## a univariate series
plot(Z1)
Z2 <- varima.sim(n=400,k=2) ## a bivariate series
plot(Z2)
Z3 <- varima.sim(n=400,k=5) ## a multivariate series of dimension 5
plot(Z3)
#################################################################################
# Simulate MA(1) where innovation series is provided via argument innov #
#################################################################################
set.seed(1234)
n <- 200
ma <- 0.6
Z<-varima.sim(list(ma=ma),n=n,innov=rnorm(n),innov.dist="bootstrap")
plot(Z)
#################################################################################
# Simulate seasonal ARIMA(2,1,0)*(0,2,1)_12 process with ar=c(1.3,-0.35), #
# ma.season = 0.8. Gaussian innovations. The series is truncated at lag 50 #
#################################################################################
set.seed(12834)
n <- 100
ar <- c(1.3, -0.35)
ma.season <- 0.8
Z<-varima.sim(list(ar=ar,d=1,sma=ma.season,D=2),n=n,trunc.lag=50)
plot(Z)
acf(Z)
#################################################################################
# Simulate seasonal ARMA(0,0,0)*(2,0,0)_4 process with intercept = 0.8 #
# ar.season = c(0.9,-0.9), period = 4, t5-distribution innovations with df = 3 #
#################################################################################
set.seed(1234)
n <- 200
ar.season <- c(0.9,-0.9)
Z<-varima.sim(list(sar=ar.season,period=4),n=n,constant=0.8,innov.dist="t",dft=3)
plot(Z)
acf(Z)
arima(Z,order=c(0,0,0),seasonal = list(order = c(2,0,0),period=4))
#################################################################################
# Simulate a bivariate white noise series from a multivariate t4-distribution #
# Then use the nonparametric bootstrap method to generate a seasonal SVARIMA #
# of order (0,d,0)*(0,0,1)_12 with d = c(1, 0), n= 250, k = 2, and #
# ma.season=array(c(0.5,0.4,0.1,0.3),dim=c(k,k,1)) #
#################################################################################
set.seed(1234)
Z1 <- varima.sim(n=250,k=2,innov.dist="t",dft=4)
ma.season=array(c(0.5,0.4,0.1,0.3),dim=c(2,2,1))
Z2 <- varima.sim(list(sma=ma.season,d=c(1,0)),n=250,k=2,
innov=Z1,innov.dist="bootstrap")
plot(Z2)
#################################################################################
# Simulate a bivariate VARIMA(2,d,1) process with length 300, where d=(1,2). #
# ar = array(c(0.5,0.4,0.1,0.5,0,0.3,0,0),dim=c(k,k,2)), #
# ma = array(c(0,0.25,0,0), dim=c(k,k,1)). #
# innovations are generated from multivariate normal #
# The process have mean zero and no deterministic terms. #
# The variance covariance is sigma = matrix(c(1,0.71,0.71,2),2,2). #
# The series is truncated at default value: trunc.lag=ceiling(100/3)=34 #
#################################################################################
set.seed(1234)
k <- 2
n <- 300
ar <- array(c(0.5,0.4,0.1,0.5,0,0.3,0,0),dim=c(k,k,2))
ma <- array(c(0,0.25,0,0),dim=c(k,k,1))
d <- c(1,2)
sigma <- matrix(c(1,0.71,0.71,2),k,k)
Z <- varima.sim(list(ma=ar,ma=ma,d=d),n=n,k=2,sigma=sigma)
plot(Z)
#################################################################################
# Simulate a trivariate Vector SVARMA(1,0,0)*(1,0,0)_12 process with length 300 #
# ar = array(c(0.5,0.4,0.1,0.5,0,0.3,0,0,0.1), dim=c(k,k,1)), where k =3 #
# ar.season = array(c(0,0.25,0,0.5,0.1,0.4,0,0.25,0.6), dim=c(k,k,1)). #
# innovations are generated from multivariate normal distribution #
# The process have mean c(10, 0, 12), #
# Drift equation a + b * t, where a = c(2,1,5), and b = c(0.01,0.06,0) #
# The series is truncated at default value: trunc.lag=ceiling(100/3)=34 #
#################################################################################
set.seed(1234)
k <- 3
n <- 300
ar <- array(c(0.5,0.4,0.1,0.5,0,0.3,0,0,0.1),dim=c(k,k,1))
ar.season <- array(c(0,0.25,0,0.5,0.1,0.4,0,0.25,0.6),dim=c(k,k,1))
constant <- c(2,1,5)
trend <- c(0.01,0.06,0)
demean <- c(10,0,12)
Z <- varima.sim(list(ar=ar,sar=ar.season),n=n,k=3,constant=constant,
trend=trend,demean=demean)
plot(Z)
acf(Z)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.