Modified dynamic programming approach for offline segmentation of long hydrometeorological time series


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

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, vol.24, no.5, pp.547-557, 2010 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 24 Issue: 5
  • Publication Date: 2010
  • Doi Number: 10.1007/s00477-009-0335-x
  • Title of Journal : STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • Page Numbers: pp.547-557

Abstract

For the offline segmentation of long hydrometeological time series, a new algorithm which combines the dynamic programming with the recently introduced remaining cost concept of branch-and-bound approach is developed. The algorithm is called modified dynamic programming (mDP) and segments the time series based on the first-order statistical moment. Experiments are performed to test the algorithm on both real world and artificial time series comprising of hundreds or even thousands of terms. The experiments show that the mDP algorithm produces accurate segmentations in much shorter time than previously proposed segmentation algorithms.

For the offline segmentation of long hydrometeological time series, a new algorithm which combines the dynamic programming with the recently introduced remaining cost concept of branch-and-bound approach is developed. The algorithm is called modified dynamic programming (mDP) and segments the time series based on the first-order statistical moment. Experiments are performed to test the algorithm on both real world and artificial time series comprising of hundreds or even thousands of terms. The experiments show that the mDP algorithm produces accurate segmentations in much shorter time than previously proposed segmentation algorithms.