scholarly journals Multiple change-point detection for non-stationary time series using Wild Binary Segmentation

2017 ◽  
Author(s):  
Karolos Korkas ◽  
Piotr Fryzlewicz
Keyword(s):  
Time Series ◽  
Change Point ◽  
Change Point Detection ◽  
Stationary Time Series ◽  
Stationary Time ◽  
Binary Segmentation ◽  
Point Detection

scholarly journals METHOD FOR CHANGE-POINT DETECTION IN PIECEWISE STATIONARY TIME-SERIES

2017 ◽  
Vol 0 (16) ◽  
pp. 120-127
Author(s):  
Олексій Володимирович Дергунов ◽  
Ганна Вадимівна Мартинюк
Keyword(s):  
Time Series ◽  
Change Point ◽  
Change Point Detection ◽  
Stationary Time Series ◽  
Stationary Time ◽  
Point Detection

BOOTSTRAP INFERENCE FOR MULTIPLE CHANGE-POINTS IN TIME SERIES

2021 ◽  
pp. 1-41
Author(s):  
Wai Leong Ng ◽  
Shenyi Pan ◽  
Chun Yip Yau
Keyword(s):  
Time Series ◽  
Change Point ◽  
Financial Time Series ◽  
Change Point Detection ◽  
Stationary Time Series ◽  
Change Points ◽  
Finite Sample ◽  
Sample Distribution ◽  
Stationary Time ◽  
Point Estimators

In this paper, we propose two bootstrap procedures, namely parametric and block bootstrap, to approximate the finite sample distribution of change-point estimators for piecewise stationary time series. The bootstrap procedures are then used to develop a generalized likelihood ratio scan method (GLRSM) for multiple change-point inference in piecewise stationary time series, which estimates the number and locations of change-points and provides a confidence interval for each change-point. The computational complexity of using GLRSM for multiple change-point detection is as low as $O(n(\log n)^{3})$ for a series of length n. Extensive simulation studies are provided to demonstrate the effectiveness of the proposed methodology under different scenarios. Applications to financial time series are also illustrated.

scholarly journals Ensemble binary segmentation for irregularly spaced data with change-points

2021 ◽  
Author(s):  
Karolos K. Korkas
Keyword(s):  
Time Series ◽  
Change Point ◽  
R Package ◽  
Transformation Process ◽  
Change Point Detection ◽  
Change Points ◽  
Segmentation Method ◽  
Binary Segmentation ◽  
A New Technique ◽  
Point Detection

AbstractWe propose a new technique for consistent estimation of the number and locations of the change-points in the structure of an irregularly spaced time series. The core of the segmentation procedure is the ensemble binary segmentation method (EBS), a technique in which a large number of multiple change-point detection tasks using the binary segmentation method are applied on sub-samples of the data of differing lengths, and then the results are combined to create an overall answer. We do not restrict the total number of change-points a time series can have, therefore, our proposed method works well when the spacings between change-points are short. Our main change-point detection statistic is the time-varying autoregressive conditional duration model on which we apply a transformation process in order to decorrelate it. To examine the performance of EBS we provide a simulation study for various types of scenarios. A proof of consistency is also provided. Our methodology is implemented in the R package , available to download from CRAN.

Sign in / Sign up

Export Citation Format

Share Document