Khaleghi, Azedeh and Ryabko, Daniil (2012) Locating changes in highly-dependent data with an unknown number of change-points. In: Advances in Neural Information Processing Systems 25 (NIPS 2012) :. UNSPECIFIED, USA, pp. 1-9. ISBN 9781627480031
Full text not available from this repository.Abstract
The problem of multiple change point estimation is considered for sequences with unknown number of change points. A consistency framework is suggested that is suitable for highly dependent time-series, and an asymptotically consistent algorithm is proposed. In order for the consistency to be established the only assumption required is that the data is generated by stationary ergodic time-series distributions. No modeling, independence or parametric assumptions are made; the data are allowed to be dependent and the dependence can be of arbitrary form. The theoretical results are complemented with experimental evaluations.