  In this research, a model is proposed to learn from event log and predict
future events of a system. The proposed PEDF model learns based on events'
sequences, durations, and extra features. The PEDF model is built by a network
made of standard clusterers and classifiers, and it has high flexibility to
update the model iteratively. The model requires to extract two sets of data
from log files i.e., transition differences, and cumulative features. The model
has one layer of memory which means that each transition is dependent on both
the current event and the previous event. To evaluate the performance of the
proposed model, it is compared to the Recurrent Neural Network and Sequential
Prediction models, and it outperforms them. Since there is missing performance
measure for event log prediction models, three measures are proposed.
