  Changes, planned or unexpected, are common during the execution of real-life
processes. Detecting these changes is a must for optimizing the performance of
organizations running such processes. Most of the algorithms present in the
state-of-the-art focus on the detection of sudden changes, leaving aside other
types of changes. In this paper, we will focus on the automatic detection of
gradual drifts, a special type of change, in which the cases of two models
overlap during a period of time. The proposed algorithm relies on conformance
checking metrics to carry out the automatic detection of the changes,
performing also a fully automatic classification of these changes into sudden
or gradual. The approach has been validated with a synthetic dataset consisting
of 120 logs with different distributions of changes, getting better results in
terms of detection and classification accuracy, delay and change region
overlapping than the main state-of-the-art algorithms.
