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#Com.numericalmethod.suanshu.stats.timeseries.linear.multivariate Classes and Interfaces - 21 results found.
| Name | Description | Type | Package | Framework |
| MultivariateAutoCorrelationFunction | This is the auto-correlation function of a multi-dimensional time series {Xt}. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate | SuanShu |
| MultivariateAutoCovarianceFunction | This is the auto-covariance function of a multi-dimensional time series {Xt}, K(i, j) = E((X_i - mu_i) imes (X_j - mu_j)') | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate | SuanShu |
| 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 |
| VARIMAModel | An ARIMA(p, d, q) process, Yt, is such that X_t = (1 - L)^d Y_t | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima | SuanShu |
| VARIMASim | This class simulates a multivariate ARIMA process. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima | SuanShu |
| VARIMAXModel | The ARIMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARIMA model by incorporating exogenous variables. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima | 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 |