Fast segmentation algorithms for long hydrometeorological time series

Aksoy H., Gedikli A., Ünal N. E., Kehagias A.

HYDROLOGICAL PROCESSES, cilt.22, sa.23, ss.4600-4608, 2008 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 22 Sayı: 23
  • Basım Tarihi: 2008
  • Doi Numarası: 10.1002/hyp.7064
  • Dergi Adı: HYDROLOGICAL PROCESSES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.4600-4608
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

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 performs segmentations satisfactorily and fast. (c) Copyright 2008 John Wiley & Sons, Ltd.

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.