Fast segmentation algorithms for long hydrometeorological time series

Hafzullah Aksoy*, Abdullah Gedikli, N. Erdem Unal, Athanasios Kehagias

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)4600-4608
Number of pages9
JournalHydrological Processes
Volume22
Issue number23
DOIs
Publication statusPublished - 15 Nov 2008

Keywords

  • Branch-and-bound approach
  • Change point
  • Dynamic programming
  • Segmentation
  • Time series

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