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#Com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess Classes and Interfaces - 16 results found.
NameDescriptionTypePackageFramework
MultivariateForecastOneStepThe innovation algorithm is an efficient way to obtain a one step least square linear predictor for a multivariate linear time seriesClasscom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocessSuanShu
MultivariateInnovationAlgorithmClasscom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocessSuanShu
VARFitThis class construct a VAR model by estimating the coefficients using OLS regression.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShu
VARLinearRepresentationThe linear representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of AR terms.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShu
VARMAAutoCorrelationCompute the Auto-Correlation Function (ACF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that This implementation solves the Yule-Walker equation.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShu
VARMAAutoCovarianceCompute the Auto-CoVariance Function (ACVF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that This implementation solves the Yule-Walker equation.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShu
VARMAForecastOneStepThis is an implementation, adapted for an ARMA process, of the innovation algorithm, which is an efficient way of obtaining a one step least square linear predictor.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShu
VARMAModelA multivariate ARMA model, Xt, takes this form.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShu
VARMAXModelThe ARMAX model (ARMA model with eXogenous inputs) is a generalization of the ARMA model by incorporating exogenous variables.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShu
VARModelThis class represents a VAR model.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShu
VARXModelA VARX (Vector AutoRegressive model with eXogeneous inputs) model, Xt, takes Y_t = mu + Sigma phi_i * Y_{t-i} + Psi * D_t + epsilon_tClasscom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShu
VECMA Vector Error Correction Model (VECM(p)) has one of the following specifications: Delta Y_t = mu + Pi Y_{t-1} + sum left ( Gamma_i Y_{t-1} ight ) + Psi D_t + epsilon_t, i = 1, 2, .Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShu
VECMLongrunThe long-run Vector Error Correction Model (VECM(p)) takes this form.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShu
VECMTransitoryA transitory Vector Error Correction Model (VECM(p)) takes this form.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShu
VMAInvertibilityThe inverse representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of the Moving Averages.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShu
VMAModelThis class represents a multivariate MA model.Classcom.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.armaSuanShu