A novel trend based SAX reduction technique for time series

作者：

Highlights：

• We propose a novel trend based SAX reduction technique for time series.

• The technique captures the trends in a time series based on abrupt change points.

• The technique is endowed with a new distance between symbolic sequences.

• The proposed distance lower bounds the Euclidean distance.

• The technique has better classification results than some related techniques.

摘要

•We propose a novel trend based SAX reduction technique for time series.•The technique captures the trends in a time series based on abrupt change points.•The technique is endowed with a new distance between symbolic sequences.•The proposed distance lower bounds the Euclidean distance.•The technique has better classification results than some related techniques.

论文关键词：Trend,Reduction,Time series,Symbolic sequences,Classification

论文评审过程：Received 13 December 2018, Revised 11 April 2019, Accepted 11 April 2019, Available online 12 April 2019, Version of Record 18 April 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.04.026