Name | Description | Type | Package | Framework |
MultivariateForecastOneStep | The innovation algorithm is an efficient way to obtain a one step least square linear predictor for a multivariate linear time series | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess | SuanShu |
|
MultivariateInnovationAlgorithm | | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess | SuanShu |
|
VARFit | This class construct a VAR model by estimating the coefficients using OLS regression. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
|
VARLinearRepresentation | The linear representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of AR terms. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
|
VARMAAutoCorrelation | Compute the Auto-Correlation Function (ACF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that This implementation solves the Yule-Walker equation. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
|
VARMAAutoCovariance | Compute the Auto-CoVariance Function (ACVF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that This implementation solves the Yule-Walker equation. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
|
VARMAForecastOneStep | This 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. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
|
VARMAModel | A multivariate ARMA model, Xt, takes this form. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
|
VARMAXModel | The ARMAX model (ARMA model with eXogenous inputs) is a generalization of the ARMA model by incorporating exogenous variables. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
|
VARModel | This class represents a VAR model. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
|
VARXModel | A VARX (Vector AutoRegressive model with eXogeneous inputs) model, Xt, takes Y_t = mu + Sigma phi_i * Y_{t-i} + Psi * D_t + epsilon_t | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
|
VECM | A 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, . | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
|
VECMLongrun | The long-run Vector Error Correction Model (VECM(p)) takes this form. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
|
VECMTransitory | A transitory Vector Error Correction Model (VECM(p)) takes this form. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
|
VMAInvertibility | The inverse representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of the Moving Averages. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
|
VMAModel | This class represents a multivariate MA model. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |