| Package | Description |
|---|---|
| dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma | |
| 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.
|
| dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch |
| Class and Description |
|---|
| 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.
|
| ARMAXModel
The ARMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARMA model by incorporating exogenous variables.
|
| ARModel
This class represents an AR model.
|
| MAModel
This class represents a univariate MA model.
|
| Class and Description |
|---|
| 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.
|
| ARMAXModel
The ARMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARMA model by incorporating exogenous variables.
|
| Class and Description |
|---|
| ARMAFit
This interface represents a fitting method for estimating \(\phi , \theta , \mu , \sigma ^{2}\) in an ARMA model.
|
| ARMAForecastOneStep
Computes the one-step ahead prediction of a causal ARMA model, by the innovative algorithm.
|
| 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.
|
| ARMAXModel
The ARMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARMA model by incorporating exogenous variables.
|
| ARModel
This class represents an AR model.
|
| MAModel
This class represents a univariate MA model.
|
| Class and Description |
|---|
| 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.
|
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