WARP: time warping for periodicity detection
WARP: time warping for periodicity detection
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DOI：
10.1109/icdm.2005.152
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发表时间：
2005-11
期刊：
影响因子：
--
通讯作者：
Mohamed G. Elfeky;W. Aref;A. Elmagarmid
中科院分区：
文献类型：
--
作者：
Mohamed G. Elfeky;W. Aref;A. Elmagarmid
周期性挖掘用于预测时间序列数据的趋势。周期性检测是定期开采的重要过程，以发现潜在的周期性率。现有的周期性检测算法没有考虑到噪声的存在，这在几乎每个实际时间序列数据中都是不可避免的。在本文中，我们解决了存在噪声的情况下的周期性检测问题。我们提出了一种新的周期性检测算法，该算法有效地处理了所有类型的噪声。根据时间扭曲，提出的算法扭曲（扩展或收缩）在各个位置的时轴以最佳去除噪声。实验结果表明，所提出的算法在噪声弹性方面优于现有的周期性检测算法。
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.