Khaleghi, Azedeh and Ryabko, Daniil (2013) Nonparametric multiple change point estimation in highly dependent time series. In: Algorithmic Learning Theory : 224th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings. Lecture Notes in Computer Science . Springer, SGP, pp. 382-396. ISBN 9783642409349
Full text not available from this repository.Abstract
Given a heterogeneous time-series sample, it is required to find the points in time (called change points) where the probability distribution generating the data has changed. The data is assumed to have been generated by arbitrary, unknown, stationary ergodic distributions. No modelling, independence or mixing assumptions are made. A novel, computationally efficient, nonparametric method is proposed, and is shown to be asymptotically consistent in this general framework; the theoretical results are complemented with experimental evaluations.