WARP: Time warping for periodicity detection

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

78 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 - Fifth IEEE International Conference on Data Mining, ICDM 2005
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8-15
Number of pages8
ISBN (Print)0769522785, 9780769522784
DOIs
Publication statusPublished - 2005
Externally publishedYes
Event5th IEEE International Conference on Data Mining, ICDM 2005 - Houston, TX, United States
Duration: 27 Nov 200530 Nov 2005

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference5th IEEE International Conference on Data Mining, ICDM 2005
Country/TerritoryUnited States
CityHouston, TX
Period27/11/0530/11/05

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