% Nomenclature
% ---------------
% st1 => singleton type-1 FLS
% fcs => forecasted series
% mydata => MG time series sample data
% TO RUN
% ---------
% mydata = mgts(3000);
% n = 0;
% RMSE = ttsf(mydata)
function fcs = st1(mydata, n)
for i = 1:504 % Training Data points 504
tgd(i) = mydata(i+n);
end
k = 0;
for i = 1:500
for j = 1:5
s(i, j) = tgd(k+j);
end
k = k+1;
end
M = 500; % No. of Rule pairs
x = 0:.01:2;
% hold on;
for j = 1:M
fs1 = gaussmf(x, [0.1 s(j, 1)]);
fs2 = gaussmf(x, [0.1 s(j, 2)]);
fs3 = gaussmf(x, [0.1 s(j, 3)]);
fs4 = gaussmf(x, [0.1 s(j, 4)]);
fs5 = gaussmf(x, [0.1 s(j, 5)]);
R(j, :) = fs1.*fs2.*fs3.*fs4.*fs5;
Memg(j) = max(R(j, :));
% plot(x, R(j,:))
end
% hold off;
for j=1:M
for i = 1:201
if max(R(j, i)) == max(R(j, :))
y(j) = i*0.01;
end
end
end
% Height Defuzzification Method
Snum = 0;
Sden = 0;
for j = M
Snum = Snum+y(j)*Memg(j);
Sden = Sden + Memg(j);
end
HDfuzz = Snum/Sden;
fcs = HDfuzz;
% -----------------------------------------
% For Better Display
% -----------------------------------------
dsp = n;
disp('~~~~~~~')
disp(' ')
disp(' FCast ')
disp (dsp)
% -------------------------------------------------
% End of st1(mydata, n)
% -------------------------------------------------
