Uses of Package
dev.nm.stat.timeseries.linear.univariate.arima
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Classes in dev.nm.stat.timeseries.linear.univariate.arima used by dev.nm.stat.timeseries.linear.multivariate.arima Class Description ARIMAModel An ARIMA(p, d, q) process, Xt, is such that \[ (1 - B)^d X_t = Y_t \] where B is the backward or lag operator, d the order of difference, Yt an ARMA(p, q) process, for which \[ Y_t = \mu + \Sigma \phi_i Y_{t-i} + \Sigma \theta_j \epsilon_{t-j} + \epsilon_t, \]ARIMAXModel The ARIMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARIMA model by incorporating exogenous variables. -
Classes in dev.nm.stat.timeseries.linear.univariate.arima used by dev.nm.stat.timeseries.linear.univariate.arima Class Description ARIMAForecast.Forecast The forecast value and variance.ARIMAModel An ARIMA(p, d, q) process, Xt, is such that \[ (1 - B)^d X_t = Y_t \] where B is the backward or lag operator, d the order of difference, Yt an ARMA(p, q) process, for which \[ Y_t = \mu + \Sigma \phi_i Y_{t-i} + \Sigma \theta_j \epsilon_{t-j} + \epsilon_t, \]ARIMAXModel The ARIMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARIMA model by incorporating exogenous variables. -
Classes in dev.nm.stat.timeseries.linear.univariate.arima used by dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma Class Description ARIMAForecast.Forecast The forecast value and variance.ARIMAModel An ARIMA(p, d, q) process, Xt, is such that \[ (1 - B)^d X_t = Y_t \] where B is the backward or lag operator, d the order of difference, Yt an ARMA(p, q) process, for which \[ Y_t = \mu + \Sigma \phi_i Y_{t-i} + \Sigma \theta_j \epsilon_{t-j} + \epsilon_t, \]ARIMAXModel The ARIMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARIMA model by incorporating exogenous variables.