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java.lang.Object com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
public class ConditionalSumOfSquares
The method Conditional Sum of Squares (CSS) fits an ARIMA model by minimizing the conditional sum of squares. The CSS estimates are conditional on the assumption that the past unobserved errors are 0s. The estimation produced by CSS can be used as a starting point for a better algorithm, e.g., the maximum likelihood.
Note that the order of integration is taken as an input, not estimated.
The R equivalent function is arima
.
Constructor Summary  

ConditionalSumOfSquares(double[] x,
int p,
int d,
int q)
Fit an ARIMA model for the observations using CSS. 

ConditionalSumOfSquares(double[] x,
int p,
int d,
int q,
int maxIterations)
Fit an ARIMA model for the observations using CSS. 
Method Summary  

double 
AIC()
Compute the AIC, a model selection criterion. 
double 
AICC()
Compute the AICC, a model selection criterion. 
Matrix 
covariance()
Get the asymptotic covariance matrix of the estimated parameters, φ and θ. 
ARMAModel 
getARMAModel()
Get the fitted ARMA model. 
ARIMAModel 
getModel()
Get the fitted ARMA model. 
int 
nParams()
Get the number of parameters for the estimation/fitting. 
ImmutableVector 
stderr()
Get the asymptotic standard errors of the estimated parameters, φ and θ. 
String 
toString()

double 
var()
Get the variance of the white noise. 
Methods inherited from class java.lang.Object 

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait 
Constructor Detail 

public ConditionalSumOfSquares(double[] x, int p, int d, int q, int maxIterations)
d
is taken as an input. If the differenced input time series is not zeromean,
it is first demeaned before running the algorithm as in Brockwell and Davis. When reporting
the model, we compute the intercept to match the mean.
x
 the time series of observationsp
 the number of AR termsd
 the order of integrationq
 the number of MA termsmaxIterations
 the maximum number of iterationspublic ConditionalSumOfSquares(double[] x, int p, int d, int q)
d
is taken as an input. If the differenced input time series is not zeromean,
it is first demeaned before running the algorithm as in Brockwell and Davis. When reporting
the model, we compute the intercept to match the mean.
x
 the time series of observationsp
 the number of AR termsd
 the order of integrationq
 the number of MA termsMethod Detail 

public int nParams()
public ARIMAModel getModel()
ARMAFit
getModel
in interface ARMAFit
public ARMAModel getARMAModel()
public double var()
ARMAFit
var
in interface ARMAFit
public Matrix covariance()
covariance
in interface ARMAFit
public ImmutableVector stderr()
stderr
in interface ARMAFit
public double AIC()
AIC
in interface ARMAFit
public double AICC()
AICC
in interface ARMAFit
public String toString()
toString
in class Object


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