  Predictive monitoring is a subfield of process mining that aims to predict
how a running case will unfold in the future. One of its main challenges is
forecasting the sequence of activities that will occur from a given point in
time -- suffix prediction -- . Most approaches to the suffix prediction problem
learn to predict the suffix by learning how to predict the next activity only,
not learning from the whole suffix during the training phase. This paper
proposes a novel architecture based on an encoder-decoder model with an
attention mechanism that decouples the representation learning of the prefixes
from the inference phase, predicting only the activities of the suffix. During
the inference phase, this architecture is extended with a heuristic search
algorithm that improves the selection of the activity for each index of the
suffix. Our approach has been tested using 12 public event logs against 6
different state-of-the-art proposals, showing that it significantly outperforms
these proposals.
