How to use nuclear regression for forecasting?

Question

Forecasting

Hello.
Two independent subtasks. There is data. It is necessary:
​​1) "Smooth" the row;
2) Predict
Everything using kernel regression.
I perform smoothing using the Nadaraya-Watson formula, the Gaussian kernel. Everything is working.
I predict with the same formula - trouble. All values ​​are equal (in red):

What am I doing wrong?
UPD:

MATLAB code:

% Код для сглаживания
function [ yy_smooth ] = kernel_smoother( xx, yy, xx_smooth, h )
    yy_smooth(1:numel(xx_smooth)) = nan;    

    kernel = @(x0,x) exp(-(x0-x).^2 / 0.5 / h^2);
    
    n = numel(yy);
    % Evaluating yy_smooth
    function yhat = y(x0)
        numerator = 0;
        denominator = 0;
        for i = 1:n
            ker_x0xi = kernel(x0,xx(i))*yy(i);
            numerator = numerator + (ker_x0xi * yy(i));
            denominator = denominator + ker_x0xi;
        end
        yhat = numerator/denominator;
    end

    % Filling yy_smooth vector
    for j = 1:numel(xx_smooth)
        yy_smooth(j) = y(xx_smooth(j));
    end
end
% --------------------------
% Код для прогнозирования
% 'p' -- порядок регрессионной зависимости, т.е. сколько предыдущих значений используется для прогноза
function [ yy_forecast ] = kernel_forecaster( xx, yy, xx_forecast, h, p )
    yy_forecast = [];
    for xi = xx_forecast
        xx = xx((end-p):end);
        yy = yy((end-p):end);
        yy_forecast = [...
            yy_forecast, ...
            kernel_smoother(xx,yy,xi,h)];
        xx = [xx, xi];
        yy = [yy, yy_forecast(end)];
    end
end
% --------------------------
%Само использование
y = [10.64 10.61 10.64 10.73 10.84 10.92 11.04 11.19 11.38 11.54 11.69 11.88];
x = 1:numel(y);
xx_smoothed = 1:0.2:numel(y);
xx_forecast = x(end)+1:1:x(end)+3;
yy_smoothed = kernel_smoother(x, y, xx_smoothed, 0.7);
yy_forecast = kernel_forecaster(x,y,xx_forecast, 0.7, 4);
%Дальше идет простая визуализация

Answer 1

Your forecast is clearly not moving, it is frozen at the last value.