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时间序列数据动态时间规整相似性的定性逼近

Qualitative approximation to Dynamic TimeWarping similarity between time series data
课程网址: http://videolectures.net/qr09_strle_qad/  
主讲教师: Blaž Strle
开课单位: 卢布尔雅那大学
开课时间: 2009-07-22
课程语种: 英语
中文简介:
动态时间规整(DTW)是用于计算在不同时间或速度下发生的两个时间序列之间的相似性的方法。尽管它的有效性在几个学科中非常流行,但其O(N2)的时间复杂性使其仅在相对较短的时间序列中有用。在本文中,我们提出定性近似定性动态时间(QDTW)到DTW。 QDTW通过将时间序列长度转换为定性时间序列来缩短时间序列长度.DTW后来在定性时间序列之间进行计算。由于定性时间序列通常比其对应的数值时间序列短很多,因此时间计算的相似性显着降低。实验结果表明,运行时间改善了三个数量级,而预测精度略有下降。
课程简介: Dynamic time warping (DTW) is a method for calculating the similarity between two time series which can occur at different times or speeds. Although its effectiveness made it very popular in several disciplines, its time complexity of O(N2) makes it useful only for relatively short time series. In this paper, we propose a qualitative approximation Qualitative Dynamic Time Warping (QDTW) to DTW. QDTW reduces a time series length by transforming it to qualitative time series. DTW is later calculated between qualitative time series. As qualitative time series are normally much shorter than their corresponding numerical time series, time to compute their similarity is significantly reduced. Experimental results have shown improved running time of up to three orders of magnitude, while prediction accuracy only slightly decreased.
关 键 词: 动态时间规整; 时间序列; 定性时间序列
课程来源: 视频讲座网
最后编审: 2019-09-14:lxf
阅读次数: 69