Class MultivariateInnovationAlgorithm
- java.lang.Object
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- dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.MultivariateInnovationAlgorithm
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public class MultivariateInnovationAlgorithm extends Object
This class implements the part of the innovation algorithm that computes the prediction error covariances, V and prediction coefficients Θ. The coefficients depend only on the auto-covariance function and time horizon, not on any particular time series data.- See Also:
- "P. J. Brockwell and R. A. Davis, "Proposition 5.2.2, Chapter 5, Multivariate Time Series," Time Series: Theory and Methods, Springer, 2006."
- "P. J. Brockwell and R. A. Davis, "Proposition 11.4.2, Chapter 11.4, Best Linear Predictors of Second Order Random Vectors," Time Series: Theory and Methods, Springer, 2006."
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Constructor Summary
Constructors Constructor Description MultivariateInnovationAlgorithm(int T, MultivariateAutoCovarianceFunction K)Run the Innovation Algorithm to compute the prediction parameters, V and Θ.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description ImmutableMatrixcovariance(int n)Get the covariance matrix for prediction errors at time t for x^t+1.ImmutableMatrixtheta(int i, int j)Get the coefficients of the linear predictor.
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Constructor Detail
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MultivariateInnovationAlgorithm
public MultivariateInnovationAlgorithm(int T, MultivariateAutoCovarianceFunction K)Run the Innovation Algorithm to compute the prediction parameters, V and Θ.- Parameters:
T- time series lengthK- the covariance structure of the time series
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Method Detail
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theta
public ImmutableMatrix theta(int i, int j)
Get the coefficients of the linear predictor.- Parameters:
i-i, ranging from 0 to Tj-j, ranging from 0 to T- Returns:
- Θ[i][j]; Θ[?][0] = 1
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covariance
public ImmutableMatrix covariance(int n)
Get the covariance matrix for prediction errors at time t for x^t+1.- Parameters:
n- time, ranging from 0 to t, the end of observation time- Returns:
- the covariance matrix for prediction errors at time n
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