| 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.
|
| Class and Description |
|---|
| ARIMAModel
An ARIMA(p, d, q) process, \(X_{t}\), is such that
\[
(1 - B)^d X_t = Y_t
\]
where
B is the backward or lag operator, d the order of difference,
\(Y_{t}\) 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.
|
| Class and Description |
|---|
| ARIMAForecast.Forecast
The forecast value and variance.
|
| ARIMAModel
An ARIMA(p, d, q) process, \(X_{t}\), is such that
\[
(1 - B)^d X_t = Y_t
\]
where
B is the backward or lag operator, d the order of difference,
\(Y_{t}\) 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.
|
| Class and Description |
|---|
| ARIMAForecast.Forecast
The forecast value and variance.
|
| ARIMAModel
An ARIMA(p, d, q) process, \(X_{t}\), is such that
\[
(1 - B)^d X_t = Y_t
\]
where
B is the backward or lag operator, d the order of difference,
\(Y_{t}\) 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.
|
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