An improvement of symbolic aggregate approximation distance measure for time series
An improvement of symbolic aggregate approximation distance measure for time series
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DOI：
10.1016/j.neucom.2014.01.045
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发表时间：
2014-08
期刊：
影响因子：
6
通讯作者：
Youqiang Sun;Jiuyong Li;Jixue Liu;Bing-Yu Sun;Christopher Chow
中科院分区：
文献类型：
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作者：
Youqiang Sun;Jiuyong Li;Jixue Liu;Bing-Yu Sun;Christopher Chow
符号聚集近似（SAX）作为一种主要的符号表示方法，在时间序列数据挖掘中得到了广泛的应用。然而，因为符号是从段的平均值映射的，所以SAX忽略了段中的重要信息，即段中的值变化的趋势。在某些情况下，这种错过可能会导致错误的分类，因为SAX表示无法区分具有相似平均值但不同趋势的不同时间序列。本文首先设计了一种利用线段的起点和终点来计算趋势距离的方法。然后，我们提出了一个修改的距离测量，通过整合的SAX距离与加权趋势距离。我们表明，我们的距离测量有一个更严格的下界的欧氏距离比原来的SAX。不同时间序列数据集上的实验结果表明，我们提出的表示显着优于原来的SAX表示和改进的SAX表示分类。
Symbolic Aggregate approXimation (SAX) as a major symbolic representation has been widely used in many time series data mining applications. However, because a symbol is mapped from the average value of a segment, the SAX ignores important information in a segment, namely the trend of the value change in the segment. Such a miss may cause a wrong classification in some cases, since the SAX representation cannot distinguish different time series with similar average values but different trends. In this paper, we firstly design a measure to compute the distance of trends using the starting and the ending points of segments. Then we propose a modified distance measure by integrating the SAX distance with a weighted trend distance. We show that our distance measure has a tighter lower bound to the Euclidean distance than that of the original SAX. The experimental results on diverse time series data sets demonstrate that our proposed representation significantly outperforms the original SAX representation and an improved SAX representation for classification.