Multiple Change Point Estimation in Stationary Ergodic Time Series
Machine Learning
2015-05-13 v9 Information Theory
math.IT
Statistics Theory
Statistics Theory
Abstract
Given a heterogeneous time-series sample, the objective is to find points in time (called change points) where the probability distribution generating the data has changed. The data are 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.
Cite
@article{arxiv.1203.1515,
title = {Multiple Change Point Estimation in Stationary Ergodic Time Series},
author = {Azadeh Khaleghi and Daniil Ryabko},
journal= {arXiv preprint arXiv:1203.1515},
year = {2015}
}