# SuanShu, a Java numerical and statistical library

## Package com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma

Class Summary
VARFitting This class construct a VAR model by estimating the coefficients using OLS regression.
VARLinearRepresentation The linear representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of AR terms.
VARMAAutoCorrelation Compute the Auto-Correlation Function (ACF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that EXt = 0.
VARMAAutoCovariance Compute the Auto-CoVariance Function (ACVF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that EXt = 0.
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.
VARMAModel A multivariate ARMA model, Xt, takes this form.
VARMAXModel The ARMAX model (ARMA model with eXogenous inputs) is a generalization of the ARMA model by incorporating exogenous variables.
VARModel This class represents a VAR model.
VARXModel A VARX (Vector AutoRegressive model with eXogeneous inputs) model, Xt, takes this form.
VECM A Vector Error Correction Model (VECM(p)) has one of the following specifications:

Transitory: $\Delta Y_t = \mu + \Pi Y_{t-1} + \sum \left ( \Gamma_i Y_{t-1} \right ) + \Psi D_t + \epsilon_t, i = 1, 2, ..., p-1$ or

Long-run: $\Delta Y_t = \mu + \Pi Y_{t-p} + \sum \left ( \Gamma_i Y_{t-1} \right ) + \Psi D_t + \epsilon_t, i = 1, 2, ..., p-1$ Yt, μ and εt are n-dimensional vectors.

VECMLongrun The long-run Vector Error Correction Model (VECM(p)) takes this form.
VECMTransitory A transitory Vector Error Correction Model (VECM(p)) takes this form.
VMAInvertibility The inverse representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of the Moving Averages.
VMAModel This class represents a multivariate MA model.