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

Research output: Journal contributions › Journal articles › Research › peer-review

Standard

Modified dynamic programming approach for offline segmentation of long hydrometeorological time series. / Gedikli, Abdullah; Aksoy, Hafzullah; Unal, N. Erdem et al.
In: Stochastic Environmental Research and Risk Assessment, Vol. 24, No. 5, 07.2010, p. 547-557.

Research output: Journal contributions › Journal articles › Research › peer-review

Harvard

Gedikli, A, Aksoy, H, Unal, NE & Kehagias, A 2010, 'Modified dynamic programming approach for offline segmentation of long hydrometeorological time series', Stochastic Environmental Research and Risk Assessment, vol. 24, no. 5, pp. 547-557. https://doi.org/10.1007/s00477-009-0335-x

APA

Gedikli, A., Aksoy, H., Unal, N. E., & Kehagias, A. (2010). Modified dynamic programming approach for offline segmentation of long hydrometeorological time series. Stochastic Environmental Research and Risk Assessment, 24(5), 547-557. https://doi.org/10.1007/s00477-009-0335-x

Vancouver

Gedikli A, Aksoy H, Unal NE, Kehagias A. Modified dynamic programming approach for offline segmentation of long hydrometeorological time series. Stochastic Environmental Research and Risk Assessment. 2010 Jul;24(5):547-557. doi: 10.1007/s00477-009-0335-x

Bibtex

@article{a54540faa9384e4c88d4d673388de560,
title = "Modified dynamic programming approach for offline segmentation of long hydrometeorological time series",
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.",
keywords = "Change point, Dynamic programming, Modified dynamic programming, Offline segmentation, Remaining cost concept, Time series, Chemistry",
author = "Abdullah Gedikli and Hafzullah Aksoy and Unal, {N. Erdem} and Athanasios Kehagias",
year = "2010",
month = jul,
doi = "10.1007/s00477-009-0335-x",
language = "English",
volume = "24",
pages = "547--557",
journal = "Stochastic Environmental Research and Risk Assessment",
issn = "1436-3240",
publisher = "Springer Science and Business Media Deutschland",
number = "5",

}

RIS

TY - JOUR

T1 - Modified dynamic programming approach for offline segmentation of long hydrometeorological time series

AU - Gedikli, Abdullah

AU - Aksoy, Hafzullah

AU - Unal, N. Erdem

AU - Kehagias, Athanasios

PY - 2010/7

Y1 - 2010/7

N2 - 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.

AB - 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.

KW - Change point

KW - Dynamic programming

KW - Modified dynamic programming

KW - Offline segmentation

KW - Remaining cost concept

KW - Time series

KW - Chemistry

UR - http://www.scopus.com/inward/record.url?scp=77955052703&partnerID=8YFLogxK

U2 - 10.1007/s00477-009-0335-x

DO - 10.1007/s00477-009-0335-x

M3 - Journal articles

AN - SCOPUS:77955052703

VL - 24

SP - 547

EP - 557

JO - Stochastic Environmental Research and Risk Assessment

JF - Stochastic Environmental Research and Risk Assessment

SN - 1436-3240

IS - 5

ER -

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Research output: Journal contributions › Journal articles › Research › peer-review

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