Effects of dimensionality reduction techniques on time series similarity measurements

Ghazi Al-Naymat, J. Taheri
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引用次数: 5

Abstract

Time Series are ubiquitous, hence, similarity search is one of the biggest challenges in the area of mining time series data. This is due to the vast data size, number of sequences and number of dimensions that lead to a very costly querying process. In this paper, we demonstrate, for the first time, the use of three dimensionality reduction techniques (random projection (RP), Down sampling (DS) and Averaging (Avg)) in time series similarity searches. Two different similarity measurements are used for this investigation; dynamic time warping (DTW) and Euclidean distance. A thorough study has been conducted in this paper based on very exhaustive experiments. Results show the individual performance of Avg, RP, and DS in the two similarity measurements in different dimensions. Simulation shows that a high similarity matching accuracy can still be achieved after a significant dimension reduction onto lower dimensions.
降维技术对时间序列相似性测量的影响
时间序列无处不在,因此相似性搜索是时间序列数据挖掘领域的最大挑战之一。这是由于庞大的数据大小、序列数量和维度数量导致查询过程非常昂贵。在本文中,我们首次展示了在时间序列相似性搜索中使用三维降维技术(随机投影(RP),下采样(DS)和平均(Avg))。本研究采用了两种不同的相似性测量方法;动态时间翘曲(DTW)和欧氏距离。本文在详尽的实验基础上进行了彻底的研究。结果显示了Avg、RP和DS在不同维度相似性测量中的个体表现。仿真结果表明,在较低维数上进行大幅度降维后,仍能获得较高的相似度匹配精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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