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Keogh, E.J. and Pazzani, M.J. (2000) A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases. 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Kyoto, 18-20 April 2000, 122-133.
https://doi.org/10.1007/3-540-45571-X_14

has been cited by the following article:

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

  AUTHORS: Lijuan Yan, Xiaotao Wu, Jiaqing Xiao

  KEYWORDS: Time Series, Representation, SAX, Feature Selection, Classification

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.6, June 30, 2022

  ABSTRACT: 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.