

def fit(self, start_params=None, trend='c', method='css-mle', transparams=True, solver='lbfgs', maxiter=50, full_output=1, disp=5, callback=None, **kwargs):
    '\n        Fits ARIMA(p,d,q) model by exact maximum likelihood via Kalman filter.\n\n        Parameters\n        ----------\n        start_params : array-like, optional\n            Starting parameters for ARMA(p,q).  If None, the default is given\n            by ARMA._fit_start_params.  See there for more information.\n        transparams : bool, optional\n            Whehter or not to transform the parameters to ensure stationarity.\n            Uses the transformation suggested in Jones (1980).  If False,\n            no checking for stationarity or invertibility is done.\n        method : str {\'css-mle\',\'mle\',\'css\'}\n            This is the loglikelihood to maximize.  If "css-mle", the\n            conditional sum of squares likelihood is maximized and its values\n            are used as starting values for the computation of the exact\n            likelihood via the Kalman filter.  If "mle", the exact likelihood\n            is maximized via the Kalman Filter.  If "css" the conditional sum\n            of squares likelihood is maximized.  All three methods use\n            `start_params` as starting parameters.  See above for more\n            information.\n        trend : str {\'c\',\'nc\'}\n            Whether to include a constant or not.  \'c\' includes constant,\n            \'nc\' no constant.\n        solver : str or None, optional\n            Solver to be used.  The default is \'lbfgs\' (limited memory\n            Broyden-Fletcher-Goldfarb-Shanno).  Other choices are \'bfgs\',\n            \'newton\' (Newton-Raphson), \'nm\' (Nelder-Mead), \'cg\' -\n            (conjugate gradient), \'ncg\' (non-conjugate gradient), and\n            \'powell\'. By default, the limited memory BFGS uses m=12 to\n            approximate the Hessian, projected gradient tolerance of 1e-8 and\n            factr = 1e2. You can change these by using kwargs.\n        maxiter : int, optional\n            The maximum number of function evaluations. Default is 50.\n        tol : float\n            The convergence tolerance.  Default is 1e-08.\n        full_output : bool, optional\n            If True, all output from solver will be available in\n            the Results object\'s mle_retvals attribute.  Output is dependent\n            on the solver.  See Notes for more information.\n        disp : bool, optional\n            If True, convergence information is printed.  For the default\n            l_bfgs_b solver, disp controls the frequency of the output during\n            the iterations. disp < 0 means no output in this case.\n        callback : function, optional\n            Called after each iteration as callback(xk) where xk is the current\n            parameter vector.\n        kwargs\n            See Notes for keyword arguments that can be passed to fit.\n\n        Returns\n        -------\n        `statsmodels.tsa.arima.ARIMAResults` class\n\n        See also\n        --------\n        statsmodels.base.model.LikelihoodModel.fit : for more information\n            on using the solvers.\n        ARIMAResults : results class returned by fit\n\n        Notes\n        ------\n        If fit by \'mle\', it is assumed for the Kalman Filter that the initial\n        unkown state is zero, and that the inital variance is\n        P = dot(inv(identity(m**2)-kron(T,T)),dot(R,R.T).ravel(\'F\')).reshape(r,\n        r, order = \'F\')\n\n        '
    mlefit = super(ARIMA, self).fit(start_params, trend, method, transparams, solver, maxiter, full_output, disp, callback, **method)
    normalized_cov_params = None
    arima_fit = ARIMAResults(self, mlefit._results.params, normalized_cov_params)
    arima_fit.k_diff = self.k_diff
    arima_fit.mle_retvals = mlefit.mle_retvals
    arima_fit.mle_settings = mlefit.mle_settings
    return ARIMAResultsWrapper(arima_fit)

dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/tsa/arima_model.py ARIMA.fit/VarMisuse@81/1307 `kwargs`->`method`