Time series regression I m new in the domain of machine learning. I m here to ask for some elucidation.
I have a data set presented as a time series( from a strain sensor coming from a wind turbine). In this time series, we can capture stop and start as u can see in the attached figure . I would like to know. which type of layer should I use? is it possible to make a model that counts up the start and stop of the signal(output dimension = 2)? Until now I just did classification using conv1D and the classes are (there is a stop, there is a start, there is nothing) but this way proceed is not convenient since sometimes we can have a rotor stop and start.
I already thanks you a lot for your help,
Counting of events is probably best done as a separate software component that takes the output of your event classifier. The event classifier model would output for each time-step two probabilities: one for started and one for stopped.

The easiest way to count the events to have a state machine that compares the probabilities against a fixed threshold to create discrete events. And once you have discrete events, counting is trivial. Pseudo-code below:

# returns a tuple with (new_state, event_name)
# event_name can be None if no event occurred
def next_event(state, start_prob, stop_prob):
    start_threshold = 0.5
    stop_threshold = 0.5

    if state == 'running':
        if stop_prob &gt; stop_threshold:
           return ("parked", "stop")

    if state == 'stopped' or state == 'unknown':
        if start_prob &gt; start_threshold:
           return ("running", "start")

    return (state, None) # no change

...
# initialization
start_counts = 0
state = 'unknown'

...
# for every new time window X
   event_prob = model.predict(X)

   state, event = next_event(state, event_prob[0], event_prob[1])
   if event is not None and event == 'start':
        start_counts += 1
   ....