I. Non-linear estimators: instead of straight lines, user some sort of interpolation to create the graph :
a. If the number of points is fixed around 10, we could use newton interpolation or chebyshev polynomials. b. For larger number of more dynamic reference points we could use cubic or quadric splines - these have the benefit that when one reference point moves only its immediate neighbors need to be re-calculated which spares us some cpu time while providing even better interpolation than the polynomials. c. If after some experimental data we notice that the reference points tend to form periods, we could even use FFT for the very best of the interpolating world ;).
these are only the interpolations that I could remember from a Numerical Methods course I took last year; when the textbook finds itself I'll probably come up with more information. Each of the above methods beats using straight lines by far though.
AND/OR
II Dynamic number of reference points based on specific events:
a. adding new reference point for every new sample (yeah right)
b. adding new reference point only if the estimate turns out to be 9x% accurate and removing one if the estimate is less than x% accurate
c. adding a new ref point if the sample is x distance from the specialization of the other reference points (to encourage multiple specialization). and removing one if it they start clustering too much
d. the opposite of c
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