I am a bit new to classification and ML. I have a dataset that I'm not sure how to handle in regards to the sampling rate of the sensors I am working with, and the time length that each 'event' lasts.
For example, I have 3 sensors that all gather data on 1 event.
Sensor 1 samples at around each .25 second (but it is not exact)
Sensor 2 samples at around each .0125 seconds (again, not exact)
Sensor 3 samples at around each second (not exact).
Each event may be a variable time length (i.e. 2 seconds to 16 seconds).
Currently I am treating each sensor reading of an event as one training example (i.e. For one event, sensor 1's reading and data are fed in as a separate training examples than sensor 2's reading and data)
As above, sensor 2 has a lot more data points per training example than sensor 1. I have time stamps for each reading, but since they don't line up smoothly with the rest of the sensors, I am not sure how to handle it. I am also unsure how to handle it over multiple events as well.
I had considered zero padding the data to just make them all the same length when feeding them into an LSTM, but from what I could see, this was advised against, so I am unsure of where to go from there.
Thanks in advance!