Goal: Classify time series data from a wind turbine as being anomalous or non-anomalus in real time and from there predict what the anomaly is in a later, more refined, model.
I have a CSV file with 6 columns corresponding to 6 sensors at uniform measurement. I know I need to separate these into separate vectors to vectors/tensors to run them through a 6 input NN.
My question is how do I create an LSTM that classifies data instead of predicting a value? Using a sigmoid?