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Segmentation of geophysical data: a big data friendly approach

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journal contribution
posted on 06.12.2017, 00:00 by David Stockwell, Ligang Zhang, Brijesh Verma
A new scalable segmentation algorithm is proposed in this paper for the forensic determination of level shifts in geophysical time series. While a number of segmentation algorithms exist, they are generally not ’big data friendly’ due either to quadratic scaling of computation time in the length of the series N or subjective penalty parameters. The proposed algorithm is called SumSeg as it collects a table of potential break points via iterative ternary splits on the extreme values of the scaled partial sums of the data. It then filters the break points on their statistical significance and peak shape. Our algorithm is linear in N and logarithmic in the number of breaks B, while returning a flexible nested segmentation model that can be objectively evaluated using the area under the receiver operator curve (AUC). We demonstratethe comparative performance of SumSeg against three other algorithms. SumSeg is available as an R package from the development site at http://github.com/davids99us/anomaly.

History

Volume

53

Start Page

39

End Page

47

Number of Pages

9

ISSN

1877-0509

Location

Netherlands

Publisher

Elsevier

Language

en-aus

Peer Reviewed

Yes

Open Access

Yes

External Author Affiliations

Centre for Intelligent Systems (2015- ); School of Engineering and Technology (2013- );

Era Eligible

Yes

Journal

Procedia computer science.

Exports