How to detect trend changes in noise time series? wget -q -O- https://i.stack.imgur.com/xczQ1.gif | tail -c +43

R> with(f, plot(x, log10(y), type='l'))

I have a series whose data is above. It obviously has two parts (separated at around x=1100). What is the most appropriate way to detect such a change in this noisy time series and fit two connected straight line segments to it?
EDIT: It looks like a method along the line of the convex hull would probably be more appropriate? For example, one can start with the lowest-most point and find the edges to the left and right of the lowest point in the convex hull. This way the two segments of support from the above of the points can be easily determined.
Any better ideas?
I don't think the method mentioned by DavidGibson makes the most sense on this dataset.


I believe you are looking for Change point detection or Change Detection. One of the more common models for this is CUSUM Model.
There is also a pretty good notebook tutorial for Facebook's new Kats library that I recommend taking a look at.

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*Kats 202 - Detection with Kats
