Financial time series pattern matching with extended UCR Suite and Support Vector Machine

作者：

Highlights：

• We propose a classifier for subsequence pattern matching in financial time series.

• The classifier is based on extended UCR Suite and the Support Vector Machine.

• Our approach achieved significant improvement in terms of speed and accuracy.

摘要

•We propose a classifier for subsequence pattern matching in financial time series.•The classifier is based on extended UCR Suite and the Support Vector Machine.•Our approach achieved significant improvement in terms of speed and accuracy.

论文关键词：Financial time series,Subsequence matching,Perceptually important points,UCR Suite,Support Vector Machine

论文评审过程：Received 14 April 2015, Revised 14 August 2015, Accepted 14 February 2016, Available online 19 February 2016, Version of Record 4 March 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.02.017