Package dev.nm.stat.timeseries.linear.multivariate.stationaryprocess

• Class Summary
Class Description
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.