WARP

Time warping for periodicity detection

Mohamed G. Elfeky, Walid G. Aref, Ahmed Elmagarmid

Research output: Chapter in Book/Report/Conference proceedingConference contribution

60 Citations (Scopus)

Abstract

Periodicity mining is used for predicting trends in time series data. Periodicity detection is an essential process in periodicity mining to discover potential periodicity rates. Existing periodicity detection algorithms do not take into account the presence of noise, which is inevitable in almost every real-world time series data. In this paper, we tackle the problem of periodicity detection in the presence of noise. We propose a new periodicity detection algorithm that deals efficiently with all types of noise. Based on time warping, the proposed algorithm warps (extends or shrinks) the time axis at various locations to optimally remove the noise. Experimental results show that the proposed algorithm out-performs the existing periodicity detection algorithms in terms of noise resiliency.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Data Mining, ICDM
Pages138-145
Number of pages8
DOIs
Publication statusPublished - 1 Dec 2005
Externally publishedYes
Event5th IEEE International Conference on Data Mining, ICDM 2005 - Houston, TX, United States
Duration: 27 Nov 200530 Nov 2005

Other

Other5th IEEE International Conference on Data Mining, ICDM 2005
CountryUnited States
CityHouston, TX
Period27/11/0530/11/05

Fingerprint

Time series

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Elfeky, M. G., Aref, W. G., & Elmagarmid, A. (2005). WARP: Time warping for periodicity detection. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 138-145). [1565672] https://doi.org/10.1109/ICDM.2005.152

WARP : Time warping for periodicity detection. / Elfeky, Mohamed G.; Aref, Walid G.; Elmagarmid, Ahmed.

Proceedings - IEEE International Conference on Data Mining, ICDM. 2005. p. 138-145 1565672.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Elfeky, MG, Aref, WG & Elmagarmid, A 2005, WARP: Time warping for periodicity detection. in Proceedings - IEEE International Conference on Data Mining, ICDM., 1565672, pp. 138-145, 5th IEEE International Conference on Data Mining, ICDM 2005, Houston, TX, United States, 27/11/05. https://doi.org/10.1109/ICDM.2005.152
Elfeky MG, Aref WG, Elmagarmid A. WARP: Time warping for periodicity detection. In Proceedings - IEEE International Conference on Data Mining, ICDM. 2005. p. 138-145. 1565672 https://doi.org/10.1109/ICDM.2005.152
Elfeky, Mohamed G. ; Aref, Walid G. ; Elmagarmid, Ahmed. / WARP : Time warping for periodicity detection. Proceedings - IEEE International Conference on Data Mining, ICDM. 2005. pp. 138-145
@inproceedings{76a76f081be34441b58ccd6d9d80c910,
title = "WARP: Time warping for periodicity detection",
abstract = "Periodicity mining is used for predicting trends in time series data. Periodicity detection is an essential process in periodicity mining to discover potential periodicity rates. Existing periodicity detection algorithms do not take into account the presence of noise, which is inevitable in almost every real-world time series data. In this paper, we tackle the problem of periodicity detection in the presence of noise. We propose a new periodicity detection algorithm that deals efficiently with all types of noise. Based on time warping, the proposed algorithm warps (extends or shrinks) the time axis at various locations to optimally remove the noise. Experimental results show that the proposed algorithm out-performs the existing periodicity detection algorithms in terms of noise resiliency.",
author = "Elfeky, {Mohamed G.} and Aref, {Walid G.} and Ahmed Elmagarmid",
year = "2005",
month = "12",
day = "1",
doi = "10.1109/ICDM.2005.152",
language = "English",
isbn = "0769522785",
pages = "138--145",
booktitle = "Proceedings - IEEE International Conference on Data Mining, ICDM",

}

TY - GEN

T1 - WARP

T2 - Time warping for periodicity detection

AU - Elfeky, Mohamed G.

AU - Aref, Walid G.

AU - Elmagarmid, Ahmed

PY - 2005/12/1

Y1 - 2005/12/1

N2 - Periodicity mining is used for predicting trends in time series data. Periodicity detection is an essential process in periodicity mining to discover potential periodicity rates. Existing periodicity detection algorithms do not take into account the presence of noise, which is inevitable in almost every real-world time series data. In this paper, we tackle the problem of periodicity detection in the presence of noise. We propose a new periodicity detection algorithm that deals efficiently with all types of noise. Based on time warping, the proposed algorithm warps (extends or shrinks) the time axis at various locations to optimally remove the noise. Experimental results show that the proposed algorithm out-performs the existing periodicity detection algorithms in terms of noise resiliency.

AB - Periodicity mining is used for predicting trends in time series data. Periodicity detection is an essential process in periodicity mining to discover potential periodicity rates. Existing periodicity detection algorithms do not take into account the presence of noise, which is inevitable in almost every real-world time series data. In this paper, we tackle the problem of periodicity detection in the presence of noise. We propose a new periodicity detection algorithm that deals efficiently with all types of noise. Based on time warping, the proposed algorithm warps (extends or shrinks) the time axis at various locations to optimally remove the noise. Experimental results show that the proposed algorithm out-performs the existing periodicity detection algorithms in terms of noise resiliency.

UR - http://www.scopus.com/inward/record.url?scp=34548544894&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34548544894&partnerID=8YFLogxK

U2 - 10.1109/ICDM.2005.152

DO - 10.1109/ICDM.2005.152

M3 - Conference contribution

SN - 0769522785

SN - 9780769522784

SP - 138

EP - 145

BT - Proceedings - IEEE International Conference on Data Mining, ICDM

ER -