Fast segmentation algorithms for long hydrometeorological time series

A time series with natural or artificially created inhomogeneities can be segmented into parts with different statistical characteristics. In this study, three algorithms are presented for time series segmentation; the first is based on dynamic programming and the second and the third--the latter be...

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Bibliographic Details
Published in:Hydrological processes 2008-11, Vol.22 (23), p.4600-4608
Main Authors: Aksoy, Hafzullah, Gedikli, Abdullah, Unal, N. Erdem, Kehagias, Athanasios
Format: Article
Language:English
Subjects:
branch-and-bound approach
change point
dynamic programming
Earth sciences
Earth, ocean, space
Exact sciences and technology
Hydrology
Hydrology. Hydrogeology
segmentation
time series
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Summary:A time series with natural or artificially created inhomogeneities can be segmented into parts with different statistical characteristics. In this study, three algorithms are presented for time series segmentation; the first is based on dynamic programming and the second and the third--the latter being an improved version of the former--are based on the branch-and-bound approach. The algorithms divide the time series into segments using the first order statistical moment (average). Tested on real world time series of several hundred or even over a thousand terms the algorithms perform segmentation satisfactorily and fast. Copyright © 2008 John Wiley & Sons, Ltd.
ISSN:0885-6087
1099-1085
DOI:10.1002/hyp.7064