Understanding how keras LSTM binary classification works using data coming from sensor
I want to use binary classification for a time series prediction: if a signal coming from a sensor changes from one shape to another, be able to detect that the system has degraded. I have two possible outputs/labels: 0 or 1. I label it with 0 when the full signal looks like this (the signal is fine):