Multiple Change Point Estimation in Stationary Ergodic Time Series

03/07/2012

∙

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.

READ FULL TEXT

Multiple Change Point Estimation in Stationary Ergodic Time Series

A consistent clustering-based approach to estimating the number of change-points in highly dependent time-series

Asymptotic nonparametric statistical analysis of stationary time series

PyChEst: a Python package for the consistent retrospective estimation of distributional changes in piece-wise stationary time series

Distribution estimation and change-point detection for time series via DNN-based GANs

Detection and estimation of parameters in high dimensional multiple change point regression models via ℓ_1/ℓ_0 regularization and discrete optimization

Dynamic time series clustering via volatility change-points

Genetic algorithm with a Bayesian approach for the detection of multiple points of change of time series of counting exceedances of specific thresholds

Multiple Change Point Estimation in Stationary Ergodic Time Series

Related Research

A consistent clustering-based approach to estimating the number of change-points in highly dependent time-series

Asymptotic nonparametric statistical analysis of stationary time series

PyChEst: a Python package for the consistent retrospective estimation of distributional changes in piece-wise stationary time series

Distribution estimation and change-point detection for time series via DNN-based GANs

Detection and estimation of parameters in high dimensional multiple change point regression models via ℓ_1/ℓ_0 regularization and discrete optimization

Dynamic time series clustering via volatility change-points

Genetic algorithm with a Bayesian approach for the detection of multiple points of change of time series of counting exceedances of specific thresholds