Recurrent neural networks predict sequences of data. In the previous recipe, we looked at 1 point in time and determined to determine if maintenance was needed. As we saw in the first recipe when we did the data analysis the turbofan run to failure dataset is highly variable. The data reading at any point in time might indicate a need for maintenance while the next indicates that there is no need for maintenance. When determining whether or not to send a technician out having an oscillating signal can be problematic. Long Short Term Memory (LSTM) is often used with time-series data such as the turbofan run to failure dataset.
With the LSTM, we look at a series of data, similar to windowing. LSTM uses an ordered sequence to help determine, in our case, if a turbofan engine is about to fail based on the previous sequence of data.