Multiple Change-Point Estimation in Stationary Ergodic Time-Series

The multiple change-point problem is considered in the most general setting, where the only assumption made on the time-series distributions generating the data is that they are stationary ergodic. No modeling, independence or parametric assumptions are made. While the need for such a general setting is dictated by real applications, the problem of change-point estimation becomes a difficult unsupervised learning problem. In this work a novel algorithm for solving this problem is proposed, and it is shown to be asymptotically consistent under the general assumptions considered.