def fpredict(self, L: list):
    '\n    ---------------------------------------------------------------------------\n    Computes the prediction.\n\n    Parameters\n    ----------\n    L: list\n        List containing the data. It must be a two-dimensional list containing \n        multiple rows. Each row must include as first element the ordered predictor \n        and as nth elements the nth - 1 exogenous variable (nth > 2).\n\n    Returns\n    -------\n    float\n        the prediction.\n        '
    try:
        result = []
        result_tmp = 0
        for i in range(len(self.X)):
            result_tmp = 0
            for j in range(len(self.coef_[i].values['coefficient'])):
                elem = self.coef_[i].values['predictor'][j]
                if (elem.lower() == 'intercept'):
                    result_tmp += self.coef_[i].values['coefficient'][j]
                else:
                    (ni, nj) = elem[2:].split('_')
                    (ni, nj) = (int(ni), int(nj))
                    result_tmp += (self.coef_[i].values['coefficient'][j] * L[(- nj)][ni])
            result += [result_tmp]
        return result
    except:
        return None
---------------------------------------------------------------------------
Computes the prediction.

Parameters
----------
L: list
    List containing the data. It must be a two-dimensional list containing 
    multiple rows. Each row must include as first element the ordered predictor 
    and as nth elements the nth - 1 exogenous variable (nth > 2).

Returns
-------
float
    the prediction.
def fpredict(self, L: list):
    '\n    ---------------------------------------------------------------------------\n    Computes the prediction.\n\n    Parameters\n    ----------\n    L: list\n        List containing the data. It must be a two-dimensional list containing \n        multiple rows. Each row must include as first element the ordered predictor \n        and as nth elements the nth - 1 exogenous variable (nth > 2).\n\n    Returns\n    -------\n    float\n        the prediction.\n        '
    try:
        result = []
        result_tmp = 0
        for i in range(len(self.X)):
            result_tmp = 0
            for j in range(len(self.coef_[i].values['coefficient'])):
                elem = self.coef_[i].values['predictor'][j]
                if (elem.lower() == 'intercept'):
                    result_tmp += self.coef_[i].values['coefficient'][j]
                else:
                    (ni, nj) = elem[2:].split('_')
                    (ni, nj) = (int(ni), int(nj))
                    result_tmp += (self.coef_[i].values['coefficient'][j] * L[(- nj)][ni])
            result += [result_tmp]
        return result
    except:
        return None