Segmentation algorithm for long time series analysis

Abdullah Gedikli, Hafzullah Aksoy*, N. Erdem Unal

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)

Abstract

Time series analysis is an important issue in the earth science-related engineering applications such as hydrology, meteorology and environmetrics. Inconsistency and nonhomogeneity that might arise in a time series yield segments with different statistical characteristics. In this study, an algorithm based on the first order statistical moment (average) of a time series is developed and applied on five time series with length ranging from 84 items to nearly 1,300. Comparison to the existing segmentation algorithms proves the applicability and usefulness of the proposed algorithm in long hydrometeorological and geophysical time series analysis.

Original languageEnglish
Pages (from-to)291-302
Number of pages12
JournalStochastic Environmental Research and Risk Assessment
Volume22
Issue number3
DOIs
Publication statusPublished - Apr 2008

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