论文标题
修改符号汇总近似方法以捕获细分趋势信息
Modifying the Symbolic Aggregate Approximation Method to Capture Segment Trend Information
论文作者
论文摘要
符号汇总近似（SAX）是一种非常流行的符号维度降低时间序列数据，因为它比其他维度降低技术具有多个优势。它的主要优势之一是其效率，因为它使用了预定的距离。另一个主要优点是，在萨克斯地区，在缩小空间下限定义的距离度量是原始空间上定义的距离度量。这使SAX能够返回确切的结果，从而完成逐个查询任务。然而，萨克斯有固有的缺点，这是它无法捕获细分趋势信息。一些研究人员试图通过提出修改以包括趋势信息来增强萨克斯。但是，这是以牺牲萨克斯的一个或多个优势为代价的。在本文中，我们研究了SAX的三种修改，以增加趋势捕获能力。这些修改在简单性，效率以及其返回的确切结果方面保留了SAX的相同特征。它们是基于时间序列的分割的简单过程，而不是经典税中的时间序列。我们在分类任务上的45个时间序列数据集上测试了这三个修改的性能，并将其与经典税的任务进行比较。我们获得的结果表明，其中一种修改设法优于经典税，而另一个修饰比经典智力少了。
The Symbolic Aggregate approXimation (SAX) is a very popular symbolic dimensionality reduction technique of time series data, as it has several advantages over other dimensionality reduction techniques. One of its major advantages is its efficiency, as it uses precomputed distances. The other main advantage is that in SAX the distance measure defined on the reduced space lower bounds the distance measure defined on the original space. This enables SAX to return exact results in query-by-content tasks. Yet SAX has an inherent drawback, which is its inability to capture segment trend information. Several researchers have attempted to enhance SAX by proposing modifications to include trend information. However, this comes at the expense of giving up on one or more of the advantages of SAX. In this paper we investigate three modifications of SAX to add trend capturing ability to it. These modifications retain the same features of SAX in terms of simplicity, efficiency, as well as the exact results it returns. They are simple procedures based on a different segmentation of the time series than that used in classic-SAX. We test the performance of these three modifications on 45 time series datasets of different sizes, dimensions, and nature, on a classification task and we compare it to that of classic-SAX. The results we obtained show that one of these modifications manages to outperform classic-SAX and that another one slightly gives better results than classic-SAX.
