An Improved Time Series Symbolic Representation Based on Multiple Features and Vector Frequency Difference  

作　　者：Lijuan Yan Xiaotao Wu Jiaqing Xiao Lijuan Yan;Xiaotao Wu;Jiaqing Xiao(College of Mathematics and Statistics, Huanggang Normal University, Huanggang, China)

机构地区：[1]College of Mathematics and Statistics, Huanggang Normal University, Huanggang, China

出　　处：《Journal of Computer and Communications》2022年第6期44-62,共19页电脑和通信（英文）

摘　　要：Symbolic Aggregate approXimation (SAX) is an efficient symbolic representation method that has been widely used in time series data mining. Its major limitation is that it relies exclusively on the mean values of segmented time series to derive the symbols. So, many important features of time series are not considered, such as extreme value, trend, fluctuation and so on. To solve this issue, we propose in this paper an improved Symbolic Aggregate approXimation based on multiple features and Vector Frequency Difference (SAX_VFD). SAX_VFD discriminates between time series by adopting an adaptive feature selection method. Furthermore, SAX_VFD is endowed with a new distance that takes into account the vector frequency difference between the symbolic sequence. We demonstrate the utility of the SAX_VFD on the time series classification task. The experimental results show that the proposed method has a better performance in terms of accuracy and dimensionality reduction compared to the so far published SAX based reduction techniques.Symbolic Aggregate approXimation (SAX) is an efficient symbolic representation method that has been widely used in time series data mining. Its major limitation is that it relies exclusively on the mean values of segmented time series to derive the symbols. So, many important features of time series are not considered, such as extreme value, trend, fluctuation and so on. To solve this issue, we propose in this paper an improved Symbolic Aggregate approXimation based on multiple features and Vector Frequency Difference (SAX_VFD). SAX_VFD discriminates between time series by adopting an adaptive feature selection method. Furthermore, SAX_VFD is endowed with a new distance that takes into account the vector frequency difference between the symbolic sequence. We demonstrate the utility of the SAX_VFD on the time series classification task. The experimental results show that the proposed method has a better performance in terms of accuracy and dimensionality reduction compared to the so far published SAX based reduction techniques.

关 键 词：Time Series REPRESENTATION SAX Feature Selection CLASSIFICATION 

分 类 号：TN9[电子电信—信息与通信工程]