Инд. авторы:	Khaleghi A., Ryabko D.
Заглавие:	Nonparametric multiple change point estimation in highly dependent time series
Библ. ссылка:	Khaleghi A., Ryabko D. Nonparametric multiple change point estimation in highly dependent time series // Theoretical Computer Science. - 2016. - Vol.620. - P.119-133. - ISSN 0304-3975.
Внешние системы:	DOI: 10.1016/j.tcs.2015.10.041; SCOPUS: 2-s2.0-84958170755;
Реферат:	eng: 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. © 2015 Elsevier B.V.
Ключевые слова:	Time series analysis; Nonparametric methods; Multiple changes; Experimental evaluation; Ergodics; Ergodic distribution; Consistency; Computationally efficient; Change-point analysis; Unsupervised learning; Time series; Probability distributions; Unsupervised learning; Stationary ergodic time series; Consistency; Change point analysis;
Издано:	2016
Физ. характеристика:	с.119-133