Detect Gradual Increase Before a Decrease in Noisy Data
What you need to do is find a feature which separate the phenomenon you're after from the noise.
If the jump is high relative to the noise STD it means it has good SNR when you compare the instantaneous derivative to the noise.
As you figured it out, in the case of great SNR as you shown above it works pretty easily and your approach did the work.
What about cases with bad SNR?
Then you need to apply some pre processing to increase the SNR.
You can apply Low Pass Filter but it means it will blur the exact location of the &quot;Jump&quot; you're after.
A nice trick I uses once was borrowing from Image Processing a class of filters called Edge Preserving Filters.
For instant it is really easy to apply the Bilateral Filter in 1D.
While it will remove noise and improve the SNR on flat regions it won't hurt the &quot;Jumps&quot; you're after.
So after using it even signals with worse SNR then the example above will start look like the case above and make your current method viable.