Multiple-Change-Point Detection for High Dimensional Time Series via Sparsified Binary Segmentation

Cho, Haeran; Fryźlewicz, Piotr

doi:10.1111/rssb.12079

Cited by 309 publications

(318 citation statements)

References 31 publications

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“…Similar results when the changepoints are allowed to approach one another are achieved in [17]. Recent studies include many applications and it can be found in [24][25][26][27][28][29][30][31][32]. Thus, this method is now the most understood and widely cited search algorithm used within the multiple change-point literature.…”

Section: The Binary Segmentation Methodssupporting

confidence: 59%

Binary Segmentation Methods for Identifying Boundaries of Spatial Domains

Raveendran

Sofronov

2017

Annals of Computer Science and Information Systems

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Abstract-Spatial clustering is an important component of spatial data analysis which aims in identifying the boundaries of domains and their number. It is commonly used in disease surveillance, spatial epidemiology, population genetics, landscape ecology, crime analysis and many other fields. In this paper, we focus on identifying homogeneous sub-regions in binary data, which indicate the presence or absence of a certain plant species which are observed over a two-dimensional lattice. To solve this clustering problem we propose to use the change-point methodology. We develop new methods based on a binary segmentation algorithm, which is a well-known multiple change-point detection method. The proposed algorithms are applied to artificially generated data to illustrate their usefulness. Our results show that the proposed methodologies are effective in identifying multiple domains and their boundaries in two dimensional spatial data.

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Section: The Binary Segmentation Methodssupporting

confidence: 59%

Binary Segmentation Methods for Identifying Boundaries of Spatial Domains

Raveendran

Sofronov

2017

Annals of Computer Science and Information Systems

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“…In these applications, the interest was in allocating an entire time series to one of a number of classes, in contrast to our goal of segmenting a time series as it evolves. Sanderson et al (2010) developed a multivariate LSW process model, which Cho and Fryzlewicz (2015) have used in segmentation of multiple locally stationary time series, while Park et al (2014) have developed estimators for the dependence structure between the multivariate time series. This multivariate LSW model could be used to extend our approach if the structure of the multiple explanatory time series is of interest.…”

Section: Discussionmentioning

confidence: 99%

Classification of multiple time signals using localized frequency characteristics applied to industrial process monitoring

Aykroyd

Barber

Miller

2016

Computational Statistics & Data Analysis

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A general framework for regression modeling using localized frequency characteristics of explanatory variables is proposed. This novel framework can be used in any application where the aim is to model an evolving process sequentially based on multiple time series data. Furthermore, this framework allows time series to be transformed and combined to simultaneously boost important characteristics and reduce noise. A wavelet transform is used to isolate key frequency structure and perform data reduction. The method is highly adaptive, since wavelets are effective at extracting localized information from noisy data. This adaptivity allows rapid identification of changes in the evolving process. Finally, a regression model uses functions of the wavelet coefficients to classify the evolving process into one of a set of states which can then be used for automatic monitoring of the system. As motivation and illustration, industrial process monitoring using electrical tomography measurements is considered. This technique provides useful data without intruding into the industrial process. Statistics derived from the wavelet transform of the tomographic data can be enormously helpful in monitoring and controlling the process. The predictive power of the proposed approach is explored using real and simulated tomographic data. In both cases, the resulting models successfully classify different flow regimes and hence provide the basis for reliable online monitoring and control of

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“…That paper proposes a quantum immune clonal clustering algorithm based on the change detection methods to achieve the change detection with clustering, the change detection problem of area as combinatorial optimization problem. In [13], Peijun Du conducts research on the hierarchical unsupervised change detection in multi-temporal hyperspectral images. A multi-band remote sensing image change detection method based on MRF using the MRF model integration difference change information of the image of each band.…”

Section: Literature Review and Analysismentioning

confidence: 99%