Class and Description |
---|
VARIMAModel
An ARIMA(p, d, q) process, Yt, is such that
\[
X_t = (1 - L)^d Y_t
\]
where
L is the lag operator, d the order of difference,
Xt an ARMA(p, q) process, for which
\[
X_t = \mu + \Sigma \phi_i X_{t-i} + \Sigma \theta_j \epsilon_{t-j} + \epsilon_t,
\]
Xt, μ and εt are n-dimensional
vectors.
|
VARIMAXModel
The ARIMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARIMA model by
incorporating exogenous variables.
|
Class and Description |
---|
VARIMAModel
An ARIMA(p, d, q) process, Yt, is such that
\[
X_t = (1 - L)^d Y_t
\]
where
L is the lag operator, d the order of difference,
Xt an ARMA(p, q) process, for which
\[
X_t = \mu + \Sigma \phi_i X_{t-i} + \Sigma \theta_j \epsilon_{t-j} + \epsilon_t,
\]
Xt, μ and εt are n-dimensional
vectors.
|
VARIMAXModel
The ARIMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARIMA model by
incorporating exogenous variables.
|
Class and Description |
---|
VARIMAModel
An ARIMA(p, d, q) process, Yt, is such that
\[
X_t = (1 - L)^d Y_t
\]
where
L is the lag operator, d the order of difference,
Xt an ARMA(p, q) process, for which
\[
X_t = \mu + \Sigma \phi_i X_{t-i} + \Sigma \theta_j \epsilon_{t-j} + \epsilon_t,
\]
Xt, μ and εt are n-dimensional
vectors.
|
VARIMAXModel
The ARIMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARIMA model by
incorporating exogenous variables.
|
Copyright © 2010-2020 NM FinTech Ltd.. All Rights Reserved.