statsmodels.tsa.tests.test_arima.Arma.fit

Arma.fit(order=(0, 0), start_params=None, method='ls', **optkwds)

Estimate lag coefficients of an ARIMA process.

Parameters:

order : sequence

p,d,q where p is the number of AR lags, d is the number of differences to induce stationarity, and q is the number of MA lags to estimate.

method : str {“ls”, “ssm”}

Method of estimation. LS is conditional least squares. SSM is state-space model and the Kalman filter is used to maximize the exact likelihood.

rhoy0, rhoe0 : array_like (optional)

starting values for estimation

Returns:

(rh, cov_x, infodict, mesg, ier) : output of scipy.optimize.leastsq

rh :

estimate of lag parameters, concatenated [rhoy, rhoe]

cov_x :

unscaled (!) covariance matrix of coefficient estimates