marr75 t1_j3c7zik wrote on January 7, 2023 at 3:01 PM

Reply to comment by C0R0NA_CHAN in [D] Which ML model should I use to analyse and detect dip in time series sequence? by C0R0NA_CHAN

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.

C0R0NA_CHAN OP t1_j3caf7o wrote on January 7, 2023 at 3:19 PM

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".