scholarly journals Multiple-change-point detection for high dimensional time series via sparsified binary segmentation

2014 ◽  
Vol 77 (2) ◽  
pp. 475-507 ◽  
Author(s):  
Haeran Cho ◽  
Piotr Fryzlewicz
Keyword(s):  
Time Series ◽  
Change Point ◽  
Change Point Detection ◽  
High Dimensional ◽  
Binary Segmentation ◽  
Point Detection

scholarly journals WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data

2021 ◽  
Author(s):  
Kamil Faber ◽  
Roberto Corizzo ◽  
Bartlomiej Sniezynski ◽  
Michael Baron ◽  
Nathalie Japkowicz
Keyword(s):  
Time Series ◽  
Change Point ◽  
Time Series Data ◽  
Change Point Detection ◽  
Series Data ◽  
High Dimensional ◽  
Point Detection

Unsupervised online change point detection in high-dimensional time series

2019 ◽  
Vol 62 (2) ◽  
pp. 719-750 ◽  
Author(s):  
Masoomeh Zameni ◽  
Amin Sadri ◽  
Zahra Ghafoori ◽  
Masud Moshtaghi ◽  
Flora D. Salim ◽  
...  
Keyword(s):  
Time Series ◽  
Change Point ◽  
Change Point Detection ◽  
High Dimensional ◽  
Point Detection

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.

Change Point Detection for Streaming High-Dimensional Time Series

2019 ◽  
pp. 515-519 ◽  
Author(s):  
Masoomeh Zameni ◽  
Zahra Ghafoori ◽  
Amin Sadri ◽  
Christopher Leckie ◽  
Kotagiri Ramamohanarao
Keyword(s):  
Time Series ◽  
Change Point ◽  
Change Point Detection ◽  
High Dimensional ◽  
Point Detection
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