Terminator3
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I just wondering. Yesterday i made a test: using simple function y=k*x + random(30)-random(30).
Compared moving average with embedding in 8-dimensional space (and more). That method with moving N-dimensional vector to nearest N-dimensional neighbor. Results are interesting, but pretty useless, as moving average gives better result. Does it mean, that i need multiple measurements of same function + noise to get better results than moving average? If anyone know some useful material on this topic please provide authors or paper name i can search for.
Compared moving average with embedding in 8-dimensional space (and more). That method with moving N-dimensional vector to nearest N-dimensional neighbor. Results are interesting, but pretty useless, as moving average gives better result. Does it mean, that i need multiple measurements of same function + noise to get better results than moving average? If anyone know some useful material on this topic please provide authors or paper name i can search for.
