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): \(\{x_{t}\}\) can be non-stationary.
MultivariateInnovationAlgorithm	This class implements the part of the innovation algorithm that computes the prediction error covariances, V and prediction coefficients Θ.