| Modifier and Type | Class and Description |
|---|---|
class |
VARIMAModel
An ARIMA(p, d, q) process, \(Y_{t}\), is such that
\[
X_t = (1 - L)^d Y_t
\]
where
L is the lag operator, d the order of difference,
\(X_{t}\) an ARMA(p, q) process, for which
\[
X_t = \mu + \Sigma \phi_i X_{t-i} + \Sigma \theta_j \epsilon_{t-j} + \epsilon_t,
\]
\(X_{t}\), μ and \(\epsilon _{t}\) are n-dimensional
vectors.
|
| Constructor and Description |
|---|
VARIMAXModel(VARIMAXModel that)
Copy constructor.
|
| Modifier and Type | Class and Description |
|---|---|
class |
VARFit
This class construct a VAR model by estimating the coefficients using OLS regression.
|
class |
VARLinearRepresentation
The linear representation of an Autoregressive Moving Average (ARMA) model is a (truncated)
infinite sum of AR terms.
|
class |
VARMAModel
A multivariate ARMA model, \(X_{t}\), takes this form.
\[
X_t = \mu + \Sigma \phi_i X_{t-i} + \Sigma \theta_j \epsilon_{t-j} + \epsilon_t,
\]
|
class |
VARMAXModel
The VARMAX model (ARMA model with eXogenous inputs) is a generalization of the ARMA model by
incorporating exogenous variables.
|
class |
VARModel
This class represents a VAR model.
|
class |
VARXModel
A VARX (Vector AutoRegressive model with eXogeneous inputs) model, \(X_{t}\), takes
this form.
\[
Y_t = \mu + \Sigma \phi_i * Y_{t-i} + \Psi * D_t + \epsilon_t
\]
where \(Y_{t}\), μ and \(\epsilon _{t}\) are n-dimensional
vectors.
|
class |
VMAModel
This class represents a multivariate MA model.
|
| Modifier and Type | Class and Description |
|---|---|
class |
SimpleAR1Fit
This class does a quick AR(1) fitting to the time series, essentially
treating the returns as independent.
|
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