Event-Tree Based Sequence Mining Using LSTM Deep-Learning Model

Figure 2

The illustration of the structure of the sequence-to-sequence event-scenario prediction. The encoder model maps the states of the input sequence into a fixed-length vector-based representation. Using these vector-based representations of input events as the initial state, the decoder model determines the next event. However, using the probabilities calculated by the dense layer, not just the event with the highest probability is recorded, but event scenarios are predicted using every prediction above a predefined threshold. The StOS and EOS tags mark the start-of-sequence and end-of-sequence tags, respectively.