论文标题
极端税：时间序列分类的基于极端点的符号表示
Extreme-SAX: Extreme Points Based Symbolic Representation for Time Series Classification
论文作者
论文摘要
时间序列分类是数据挖掘的重要问题，该数据挖掘了不同域中的多个应用程序。由于时间序列数据通常是高维度的，因此已提出了降低降低技术作为降低其维度的有效方法。时间序列数据最流行的降低性缩小技术之一是符号汇总近似（SAX），它的灵感来自文本挖掘和生物信息学的算法。 SAX是简单有效的，因为它使用了预定的距离。 SAX的缺点在于它无法准确代表时间序列中的重要点。在本文中，我们介绍了极端智能（E-SAX），该及极端键（E-Sax）仅使用每个段的极端点来表示时间序列。 E-SAX具有原始SAX的简单性和效率的完全相同，但是与原始SAX相比，它在时间序列分类中获得了更好的结果，正如我们在各种时间序列数据集中进行的广泛实验所示。
Time series classification is an important problem in data mining with several applications in different domains. Because time series data are usually high dimensional, dimensionality reduction techniques have been proposed as an efficient approach to lower their dimensionality. One of the most popular dimensionality reduction techniques of time series data is the Symbolic Aggregate Approximation (SAX), which is inspired by algorithms from text mining and bioinformatics. SAX is simple and efficient because it uses precomputed distances. The disadvantage of SAX is its inability to accurately represent important points in the time series. In this paper we present Extreme-SAX (E-SAX), which uses only the extreme points of each segment to represent the time series. E-SAX has exactly the same simplicity and efficiency of the original SAX, yet it gives better results in time series classification than the original SAX, as we show in extensive experiments on a variety of time series datasets.
