Predicting Activities in Business Processes with LSTM Recurrent Neural Networks

JORGE ROA; MARIANO RUBIOLO; EDGAR TELLO-LEAL; ULISES M. RAMIREZ-ALCOCER

Santa Fe

Congreso; 2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K); 2018

ITU

The Long Short-Term Memory (LSTM) Recurrent Neural Networks provide a high precision in the prediction of sequences in several application domains. In the domain of business processes it is currently possible to exploit event logs to make predictions about the execution of cases. This article shows that LSTM networks can also be used for the prediction of execution of cases in the context of an event log that originates from the IoT and Industry 4.0 domain. This is a key aspect to provide valuable input for planning and resource allocation (either physical or virtual), since each trace associated with a case indicates the sequential execution of activities in business processes. A methodology for the implementation of an LSTM neural network is also proposed. An event log of the industry domain is used to train and test the proposed LSTM neural network. Our preliminary results indicate that the prediction of the next activity is acceptable according to the literature of the domain.