| 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.
|
| Constructor and Description |
|---|
VARIMAXModel(ARIMAXModel model)
Construct a multivariate ARIMAX model from a univariate ARIMAX model.
|
| Modifier and Type | Class and Description |
|---|---|
class |
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
\[
Y_t = \mu + \Sigma \phi_i Y_{t-i} + \Sigma \theta_j \epsilon_{t-j} + \epsilon_t,
\]
|
| Constructor and Description |
|---|
ARIMAXModel(ARIMAXModel that)
Copy constructor.
|
| 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 |
ARMAXModel
The ARMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARMA model by incorporating exogenous variables.
|
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.
|
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