Hello guys, I'm quite new to Machine Learning but I have a kind of challenge for an academic paper.
My data is a time series and I have to make predictions about specific positions in the time series. As an example, I have an array of floats with 350 positions, there is a pattern to certain positions that I need my model to figure out, based on their values and the surrounding values. In my train examples I would have the array of floats and the correct marked positions (e.g. position 35, 86, 150, 240, 351). It doesn't need to always get the exact position, but it should get as closer as possible.
Do you guys know of anything similar to this so I can study about it? Or do you recommend any approach? I'm kinda stuck on figuring out how to ascertain the loss and the precision, as it doesn't need to meet the exact position of the label, just to be as close as possible.
Narigah t1_isap81a wrote
Reply to [D] Simple Questions Thread by AutoModerator
Hello guys, I'm quite new to Machine Learning but I have a kind of challenge for an academic paper.
My data is a time series and I have to make predictions about specific positions in the time series. As an example, I have an array of floats with 350 positions, there is a pattern to certain positions that I need my model to figure out, based on their values and the surrounding values. In my train examples I would have the array of floats and the correct marked positions (e.g. position 35, 86, 150, 240, 351). It doesn't need to always get the exact position, but it should get as closer as possible.
Do you guys know of anything similar to this so I can study about it? Or do you recommend any approach? I'm kinda stuck on figuring out how to ascertain the loss and the precision, as it doesn't need to meet the exact position of the label, just to be as close as possible.
Thanks in advance for any help!