So, what is your question? Are you wanting to be able to approximate the exact solution in real-time, and not wait to post-process?
Hi all,
I have a timeseries data set that converges to the exact (attached). I would like to have some sort of tolerance in my programs but would like to do it numerically. It would be something like min(max(distance_from_exact_of_timeseries)). It is difficult to explain but I hope the picture will help. If I fit a linear regression line to the data I get something along the lines of what I am looking for but I can only do this with the tools after the program has run which defies the point.
I apologise it's probably unclear. I would like a bound. As the approximate solution oscillates closer to the exact solution I would like to input a value say 0.01. When the approximate solution gets to the point where it is oscillating at 0.01 intervals about the exact solution it stops. So I know my approximate solution is within 0.01 error of the exact.
This would be easy if it was linear because it would just be
while abs(approx-exact)>0.01
continue program
end
however in my case the approximate solution passes through the exact solution and this statement doesn't work.
Is there a way to statistically or otherwise determine the minimum maximum value
Thank you for your reply