一种更快的符号聚集近似方法
Towards a faster symbolic aggregate approximation method
摘要：
相似性搜索问题是时间序列数据采掘中的主要问题之一。传统上，这个问题是通过顺序地将给定的查询与数据库中的所有时间序列进行比较，并返回在该查询的预定阈值内的所有时间序列来解决的。但是，目前使用的时间序列数据库的大规模和高维性使得该方案效率低下。有许多表示技术旨在降低时间序列的维数，以便在低维空间级别上更快地处理搜索。符号聚集近似（SAX）是文献中最具竞争力的方法之一。在本文中，我们提出了一种新的方法，提高了性能的SAX添加到它的另一个排除条件，增加了排除功率。该方法基于使用时间序列的两种表示：一种是SAX，另一种是基于时间序列的最佳近似。预先计算的距离被离线计算和存储，以在线使用，从而使用两个排除条件排除大范围的搜索空间。实验结果表明，该方法比SAX算法更快.
英文摘要：
The similarity search problem is one of the main problems in time series data mining. Traditionally, this problem was tackled by sequentially comparing the given query against all the time series in the database, and returning all the time series that are within a predetermined threshold of that query. But the large size and the high dimensionality of time series databases that are in use nowadays make that scenario inefficient. There are many representation techniques that aim at reducing the dimensionality of time series so that the search can be handled faster at a lower-dimensional space level. The symbolic aggregate approximation (SAX) is one of the most competitive methods in the literature. In this paper we present a new method that improves the performance of SAX by adding to it another exclusion condition that increases the exclusion power. This method is based on using two representations of the time series: one of SAX and the other is based on an optimal approximation of the time series. Pre-computed distances are calculated and stored offline to be used online to exclude a wide range of the search space using two exclusion conditions. We conduct experiments which show that the new method is faster than SAX.
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