Uses of Package
dev.nm.stat.timeseries.linear.univariate.stationaryprocess
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Classes in dev.nm.stat.timeseries.linear.univariate.stationaryprocess used by dev.nm.stat.timeseries.linear.univariate.stationaryprocess Class 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. -
Classes in dev.nm.stat.timeseries.linear.univariate.stationaryprocess used by dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma Class 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.