Locating Changes in Highly Dependent Data with Unknown Number of Change Points

Part of Advances in Neural Information Processing Systems 25 (NIPS 2012)

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Azadeh Khaleghi, Daniil Ryabko


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.