LIFEWATCH: Lifelong Wasserstein Change Point Detection

Change point detection methods offer a crucial capability in modern data analysis tasks characterized by evolving time series data in the form of data streams. Recent interest in lifelong learning showed the importance of acquiring knowledge and identifying new occurring tasks in a continually evolving environment. Although this setting could benefit from a timely identification of changes, existing change point detection methods are unable to recognize recurring tasks, which is a necessary condition in lifelong learning. In this paper, we attempt to fill this gap by proposing LIFEWATCH, a novel Wasserstein-based change point detection approach with memory capable of modeling multiple data distributions in a fully unsupervised manner. Our method does not only detect changes, but discriminates between changes characterized by the appearance of a new task and changes that rather describe a recurring or previously seen task. An extensive experimental evaluation involving a large number of benchmark datasets shows that LIFEWATCH outperforms stateof-the-art methods for change detection while exploiting the characterization of detected changes to correctly identify tasks occurring in complex scenarios characterized by recurrence in lifelong consolidation settings.