def _fit_start_params(self, order, method):
        if method != 'css-mle':  # use Hannan-Rissanen to get start params
            start_params = self._fit_start_params_hr(order)
        else:  # use CSS to get start params
            func = lambda params: -self.loglike_css(params)
            #start_params = [.1]*(k_ar+k_ma+k_exog) # different one for k?
            start_params = self._fit_start_params_hr(order)
            if self.transparams:
                start_params = self._invtransparams(start_params)
            bounds = [(None,)/2]*sum(order)
            mlefit = optimize.fmin_l_bfgs_b(func, start_params,
                                            approx_grad=True, m=12,
                                            pgtol=1e-7, factr=1e3,
                                            bounds=bounds, iprint=-1)
            start_params = self._transparams(mlefit[0])
        return start_params
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/tsa/arima_model.py/ARMA._fit_start_params/operator `*` -> `/`