Uses of Class
dev.nm.stat.timeseries.linear.univariate.arima.ARIMAModel

Packages that use ARIMAModel

Package	Description
dev.nm.stat.timeseries.linear.multivariate.arima
dev.nm.stat.timeseries.linear.univariate.arima
dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma	ARMA model fitting and forecasting from an ordered observation history.

Uses of ARIMAModel in dev.nm.stat.timeseries.linear.multivariate.arima

Constructors in dev.nm.stat.timeseries.linear.multivariate.arima with parameters of type ARIMAModel

Constructor and Description
VARIMAModel(ARIMAModel model) Construct a multivariate model from a univariate ARIMA model.

Uses of ARIMAModel in dev.nm.stat.timeseries.linear.univariate.arima

Methods in dev.nm.stat.timeseries.linear.univariate.arima that return ARIMAModel

Modifier and Type	Method and Description
ARIMAModel	AutoARIMAFit.optimalModelByAIC() Selects the optimal ARIMA model by AIC.
ARIMAModel	AutoARIMAFit.optimalModelByAICC() Selects the optimal ARIMA model by AICC.

Constructors in dev.nm.stat.timeseries.linear.univariate.arima with parameters of type ARIMAModel

Constructor and Description
ARIMAForecast(IntTimeTimeSeries xt, ARIMAModel arima) Constructs a forecaster for a time series assuming ARIMA model.
ARIMAForecastMultiStep(IntTimeTimeSeries xt, ARIMAModel arima, int h) Makes the h-step ahead prediction for an ARIMA model.
ARIMAModel(ARIMAModel that) Copy constructor.
ARIMASim(ARIMAModel arima) Construct an ARIMA model, using random standard Gaussian innovations.
ARIMASim(ARIMAModel arima, double[] lags, double[] innovations, RandomNumberGenerator rng) Construct an ARIMA model.
ARIMASim(ARIMAModel arima, RandomNumberGenerator rng) Construct an ARIMA model.

Uses of ARIMAModel in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma

Subclasses of ARIMAModel in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma

Modifier and Type	Class and Description
class	ARMAModel A univariate ARMA model, \(X_{t}\), takes this form. \[ X_t = \mu + \sum_{i=1}^p \phi_i X_{t-i} + \sum_{i=1}^q \theta_j \epsilon_{t-j} + \epsilon_t \] Parameter and observation conventions The constant argument is the intercept in \(X_t=c+\sum_i\phi_i X_{t-i}+\varepsilon_t+\sum_j\theta_j\varepsilon_{t-j}\), not the unconditional mean.
class	ARModel This class represents an AR model.
class	LinearRepresentation The linear representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of AR terms.
class	MAModel This class represents a univariate MA model.

Methods in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma that return ARIMAModel

Modifier and Type	Method and Description
ARIMAModel	ARMAFit.getModel() Get the fitted ARMA model.
ARIMAModel	ConditionalSumOfSquares.getModel() Get the fitted ARIMA model.