@inproceedings{a8605e82125345649ccb7ddf857ee954,
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 Elmagarmid, {Ahmed K.}",
year = "2005",
doi = "10.1109/ICDM.2005.152",
language = "English",
isbn = "0769522785",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "8--15",
booktitle = "Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005",
address = "United States",
note = "5th IEEE International Conference on Data Mining, ICDM 2005 ; Conference date: 27-11-2005 Through 30-11-2005",
}