(self, initial_part, differenced_rest, order=None)
return(actual_predictions)
function to invert the differencing
function to invert the differencing
function to invert the differencing
def invert_differencing(self, initial_part, differenced_rest, order=None):
        """
            function to invert the differencing
        """

        if order is None: order = self.diff_order

        # compute the differenced values of the initial part:
        starting_points = [ self.apply_differencing(initial_part, order=order)[-1] for order in range(self.diff_order)]
        
        actual_predictions = differenced_rest
        import pdb
        pdb.set_trace()
        for s in starting_points[::-1]:
            actual_predictions = np.cumsum(np.hstack([s, actual_predictions]))[1:]

        return(actual_predictions)
def invert_differencing ( self , initial_part , differenced_rest , order = None ) : if order is None : order = self . diff_order # compute the differenced values of the initial part: starting_points = [ self . apply_differencing ( initial_part , order = order ) [ - 1 ] for order in range ( self . diff_order ) ] actual_predictions = differenced_rest import pdb pdb . set_trace ( ) for s in starting_points [ : : - 1 ] : actual_predictions = np . cumsum ( np . hstack ( [ s , actual_predictions ] ) ) [ 1 : ] return ( actual_predictions )