Dear all
Consider a series of data values that reflect a smooth function (e.g.
a low-degree polynomial), but there might be additional features in
the data (e.g. narrow peaks or noise). I'd like to fit a polynomial
to
this data, whereby this polynomial reflects a smooth approximation of
the minima of the raw data (I call this the "base line"). The
following might help to illustrate what I'm trying to accomplish:
x = [-1:0.01:1]; % x-axis values
p = [-3 2 1 0]; yp = polyval (p,x); % make up a polynomial
reflecting the "base line" for illustration
y = yp + rand(size(x)); % this would be the raw data
plot (x,y,x,yp); legend ('raw data','base line') % plot the raw
data and the polynomial for illustration
Has anyone an idea of how to accomplish this? Are there standard
methods? I'd appreciate any hints.
Thanks
Matthias