Class ConditionalSumOfSquares
- java.lang.Object
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- dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
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- All Implemented Interfaces:
ARMAFit
public class ConditionalSumOfSquares extends Object implements ARMAFit
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 isarima
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Constructor Summary
Constructors Constructor Description 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.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description 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 ARIMA 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.
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Constructor Detail
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ConditionalSumOfSquares
public ConditionalSumOfSquares(double[] x, int p, int d, int q, int maxIterations)
Fit an ARIMA model for the observations using CSS. Note that the algorithm fits only an ARMA model.d
is taken as an input. If the differenced input time series is not zero-mean, it is first de-mean-ed before running the algorithm as in Brockwell and Davis. When reporting the model, we compute the intercept to match the mean.- Parameters:
x
- the time series of observationsp
- the number of AR termsd
- the order of integrationq
- the number of MA termsmaxIterations
- the maximum number of iterations
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ConditionalSumOfSquares
public ConditionalSumOfSquares(double[] x, int p, int d, int q)
Fit an ARIMA model for the observations using CSS. Note that the algorithm fits only an ARMA model.d
is taken as an input. If the differenced input time series is not zero-mean, it is first de-mean-ed before running the algorithm as in Brockwell and Davis. When reporting the model, we compute the intercept to match the mean.- Parameters:
x
- the time series of observationsp
- the number of AR termsd
- the order of integrationq
- the number of MA terms
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Method Detail
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nParams
public int nParams()
Get the number of parameters for the estimation/fitting. They are the AR terms, MA terms, and variance (sigma^2).- Returns:
- the number of parameters
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getModel
public ARIMAModel getModel()
Get the fitted ARIMA model.
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getARMAModel
public ARMAModel getARMAModel()
Get the fitted ARMA model.- Returns:
- the fitted ARMA model
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var
public double var()
Description copied from interface:ARMAFit
Get the variance of the white noise.
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covariance
public Matrix covariance()
Get the asymptotic covariance matrix of the estimated parameters, φ and θ. The estimators are asymptotically normal.- Specified by:
covariance
in interfaceARMAFit
- Returns:
- the asymptotic covariance matrix
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stderr
public ImmutableVector stderr()
Get the asymptotic standard errors of the estimated parameters, φ and θ. The estimators are asymptotically normal.
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AIC
public double AIC()
Compute the AIC, a model selection criterion.- Specified by:
AIC
in interfaceARMAFit
- Returns:
- the AIC
- See Also:
- Wikipedia: Akaike information criterion
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AICC
public double AICC()
Compute the AICC, a model selection criterion.
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