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Predictive monitoring is a subfield of process mining that aims to predicthow a running case will unfold in the future. One of its main challenges isforecasting the sequence of activities that will occur from a given point intime -- suffix prediction -- . Most approaches to the suffix prediction problemlearn to predict the suffix by learning how to predict the next activity only,not learning from the whole suffix during the training phase. This paperproposes a novel architecture based on an encoder-decoder model with anattention mechanism that decouples the representation learning of the prefixesfrom the inference phase, predicting only the activities of the suffix. Duringthe inference phase, this architecture is extended with a heuristic searchalgorithm that improves the selection of the activity for each index of thesuffix. Our approach has been tested using 12 public event logs against 6different state-of-the-art proposals, showing that it significantly outperformsthese proposals.

Encoder-Decoder Model for Suffix Prediction in Predictive Monitoring