Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Nonparametric multiple change point estimation in highly dependent time series
AU - Khaleghi, Azadeh
AU - Ryabko, Daniil
PY - 2016/3/21
Y1 - 2016/3/21
N2 - 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.
AB - 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.
KW - Change point analysis
KW - Stationary ergodic time series
KW - Unsupervised learning
KW - Consistency
U2 - 10.1016/j.tcs.2015.10.041
DO - 10.1016/j.tcs.2015.10.041
M3 - Journal article
VL - 620
SP - 119
EP - 133
JO - Theoretical Computer Science
JF - Theoretical Computer Science
SN - 0304-3975
ER -