Unsupervised Change Point Detection in Multivariate Time Series

We consider the challenging problem of unsupervised change point detection in multivariate time series when the number of change points is unknown. Our method eliminates the user's need for careful parameter tuning, enhancing its practicality and usability. Our approach identifies time series segments with similar empirically estimated distributions, coupled with a novel greedy algorithm guided by the minimum description length principle. We provide theoretical guarantees and, through experiments on synthetic and real-world data, provide empirical evidence for its improved performance in identifying meaningful change points in practical settings.