Class | Description |
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MultivariateForecastOneStep |
The innovation algorithm is an efficient way to obtain
a one step least square linear predictor for a multivariate 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.
|
MultivariateInnovationAlgorithm |
This class implements the part of the innovation algorithm that computes the prediction error
covariances, V and prediction coefficients Θ.
|
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