Class and Description |
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InnovationsAlgorithm
The innovations algorithm is an efficient way to obtain a one step least square linear predictor
for a univariate linear time series with known auto-covariance and these properties (not limited
to ARMA processes):
{xt} can be non-stationary.
E(xt) = 0 for all t.
This class implements the part of the innovations algorithm that computes the prediction error
variances, v and prediction coefficients θ.
|
Class and Description |
---|
InnovationsAlgorithm
The innovations algorithm is an efficient way to obtain a one step least square linear predictor
for a univariate linear time series with known auto-covariance and these properties (not limited
to ARMA processes):
{xt} can be non-stationary.
E(xt) = 0 for all t.
This class implements the part of the innovations algorithm that computes the prediction error
variances, v and prediction coefficients θ.
|
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