Abstract: A partitioning method includes the steps of acquiring an observation matrix including time series (x1 x2, . . . , xp); for each time series calculating a distance matrix comprising distance values between the elements of the time series, then generating the primary image on the basis of the distance matrix; implementing a learning algorithm for segmenting the primary image so as to obtain a segmented image; defining, on the basis of the segmented image, a primary boundary signal representative of the boundaries; and merging the primary boundary signals in order to obtain a global boundary signal, and defining classes on the basis of the global boundary signal.