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Fast segmentation algorithms for long hydrometeorological time series
Author(s) -
Aksoy Hafzullah,
Gedikli Abdullah,
Unal N. Erdem,
Kehagias Athanasios
Publication year - 2008
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.7064
Subject(s) - series (stratigraphy) , algorithm , segmentation , hydrometeorology , computer science , moment (physics) , time series , artificial intelligence , machine learning , geology , meteorology , classical mechanics , precipitation , paleontology , physics
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.