Time series data, prevalent across scientific studies, captures the temporal evolution of phenomena such as sports activities, weather patterns, and health conditions. The fundamental task of classifying these data hinges on identifying statistical properties that can be used to predict labels. This becomes particularly challenging with long, multi-dimensional time series, leading to the necessity of a previous step, in which the segmentation into sub-series is done. This research focuses on recognizing transition between segments characterized by homogeneous trends, a critical aspect of segmentation. Transition points are often ambiguous, situated at the juncture of two states, and identifying them poses unique segmentation challenges.