WARP: Time Warping for Periodicity Detection

Get BibTex-formatted data

Author

MG Elfeky, WG Aref, AK Elmagarmid

Entry type

article

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 outperforms the existing periodicity detection algorithms in terms of noise resiliency.

Date

2005 – 11

Journal

Data Mining, Fifth IEEE International Conference on

Key alpha

Aref

Publication Date

2005-11-00

BibTex-formatted data

To refer to this entry, you may select and copy the text below and paste it into your BibTex document. Note that the text may not contain all macros that BibTex supports.

@Article{ Aref,
	title = "WARP: Time Warping for Periodicity Detection",
	author = "MG Elfeky, WG Aref, AK Elmagarmid",
	year = "2005",
	month = "11",
	journal = "Data Mining, Fifth IEEE International Conference on",
	pages = "8",
	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 outperforms the existing periodicity detection algorithms in terms of noise resiliency.",
}