0
$\begingroup$

What techniques are suitable and what do I need to learn in order to detect and count the number of "events" (pic) that consist of:

a) shape 1 3 and 4 and not of (2 and 5) or

b) of all shapes above the blue line with a certain minimum "volume" like shapes 1 and 4?

My first idea is to use a window function and somehow integrate the area but i want to be sure to use an appropriate procedure. I havent worked with time series before and I dont know were to get startet. I have to use python for this project.

enter image description here

$\endgroup$
1
$\begingroup$

Try generating a dictionary of patterns you want to identify. You can then use convolutions/ cross-correlations to identify where these patterns appear in your data. https://en.wikipedia.org/wiki/Convolution https://en.wikipedia.org/wiki/Cross-correlation http://paulbourke.net/miscellaneous/correlate/

This method is also called 'matched filter'.

$\endgroup$
1
$\begingroup$

I suggest to start with "outlier detection", "anomaly detection", filtering methods. Its pretty wide topic to cover but you need to start from somewhere.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.