class statsmodels.tsa.arima_model.ARMA(endog, order, exog=None, dates=None, freq=None, missing='none')
[source]
Autoregressive Moving Average ARMA(p,q) Model
Parameters: |
|
---|
If exogenous variables are given, then the model that is fit is
where \(\phi\) and \(\theta\) are polynomials in the lag operator, \(L\). This is the regression model with ARMA errors, or ARMAX model. This specification is used, whether or not the model is fit using conditional sum of square or maximum-likelihood, using the method
argument in statsmodels.tsa.arima_model.ARMA.fit
. Therefore, for now, css
and mle
refer to estimation methods only. This may change for the case of the css
model in future versions.
fit ([start_params, trend, method, …]) | Fits ARMA(p,q) model using exact maximum likelihood via Kalman filter. |
from_formula (formula, data[, subset, drop_cols]) | Create a Model from a formula and dataframe. |
geterrors (params) | Get the errors of the ARMA process. |
hessian (params) | Compute the Hessian at params, |
information (params) | Fisher information matrix of model |
initialize () | Initialize (possibly re-initialize) a Model instance. |
loglike (params[, set_sigma2]) | Compute the log-likelihood for ARMA(p,q) model |
loglike_css (params[, set_sigma2]) | Conditional Sum of Squares likelihood function. |
loglike_kalman (params[, set_sigma2]) | Compute exact loglikelihood for ARMA(p,q) model by the Kalman Filter. |
predict (params[, start, end, exog, dynamic]) | ARMA model in-sample and out-of-sample prediction |
score (params) | Compute the score function at params. |
endog_names | Names of endogenous variables |
exog_names |
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.tsa.arima_model.ARMA.html