- Form a template for the increasing-then-decreasing shape you are looking for, maybe about 50 units wide along your X axis. Step the template point by point along the X axis and identify the points where it gives you the best match to a segment of your noisy curve. Those "points of best match" will tell you where the peak is (because you know where the peak is in the template.
- Look for the start of a series of (say) 5 points that are all higher or lower than the average of the last (say) 3 points. Adjust the 5 and 3 to look at larger or smaller windows.
How to find the turning point from noisy data
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Hi, I wanted to extract the turning point from the noisy data like below. I tried 1st/2nd derivative, findpeak, .etc, but none of those helped. To make sure not to change the position of turning point, I don't want to smooth it. Are there some ideas about it? Many thanks!!
the raw data is attached.
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Answers (1)
Jeff Miller
on 11 Mar 2020
A few ideas:
Your eyeball automatically looks over a much wider range of points than 1st or 2nd derivative (I don't know how findpeak works), so you probably want an algorithm that also looks across a fairly wide range of points.
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