I don't know why you would need ML to do this. Detecting local minima is a Calc I homework question. Scipy (and many other libraries) do it in a single function call.

Agreed, i thought of this too. But say, i want to scale up and detect an ad in video based on intrest drop then it will be difficult to achieve that through local minima right? Interest at a particular second might drop due to boring content right? What if I want model to detect intrest drop only when ad occurs?

Normally there'll be a max drop in intrest during an ad for sure. But we can't use global minima technique here because normally people skip off end parts of video so that might actually be the biggest drop and we don't want that.

If you want a model, you will need to think about input variables... This where it gets complicated.

As it minima detection, there are many ways this can be achieved, for example: look at a sign change in viewers per unit of time? Or if your data is evenly sampled, compute the difference between viewers at t[n+1] and t[n], and see whether the sign change is stable (no flip-flop) over say 5 or 10 samples?

To get a robust detection you will need to play with your data. A lot.

The pain of building a ml model that isn't even as accurate as calculating portions below average

Idk, you can do ml but I would just recommend a simple function

I'm not following what you're saying but you can detect all local minima with a single function call, order them and know their summary statistics with a second function call, and come up with a threshold based comparison for the end of the video if that's what you want.

None of this requires a machine learning model. You lost me when you mixed in "only when an ad occurs". Do you have any data that would help you train such a model? Are you just trying to detect ads? You could:

• identify all local minima attention drops
• engineer features such as distance into video, length of drop (time spent below average before and after local minima), magnitude of drop
• perform unsupervised learning, i.e. PCA/t-sne/k-means
• hope the "structural" features identified by unsupervised learning help you organize ads vs non-ads (they might!)

Again, not a complicated system because you don't have complex features as you've described them.

Is this just a novelty project? The way you're asking about it makes me think there's a low chance of follow through and your questions are kind of "arguing" towards a more complicated model. Run whatever code you are capable of then, I guess. I will probably decline to give further advice if that trend of leading questions continues.

Thankyouuu.

Cool thanks, will try it out. This is just a side project i was trying. I just wanted to practically explore more about time series and how to pass multiple of them to a rnn model. My current objective is what I have written in the post's description. The " detecting ad" one was just a feature i thought of at that time of writing the reply. "If in future I wanted to extend the application how do I proceed with it" that's what the motive was behind asking that to you. Either way thanks, and sorry if I came out as "arguing".