Time series classification based on multi-feature dictionary representation and ensemble learning

作者：

Highlights：

• Extract both the mean and trend features based on Symbolic Aggregate approXimation.

• Design single classifier based on both the mean and trend features.

• Construct ensemble classifier by multi-feature dictionary and ensemble learning.

• Experiments on real datasets verify effectiveness of our proposal.

摘要

•Extract both the mean and trend features based on Symbolic Aggregate approXimation.•Design single classifier based on both the mean and trend features.•Construct ensemble classifier by multi-feature dictionary and ensemble learning.•Experiments on real datasets verify effectiveness of our proposal.

论文关键词：Time series classification,Bag-of-feature,Symbolic representation

论文评审过程：Received 16 July 2020, Revised 9 September 2020, Accepted 23 October 2020, Available online 5 December 2020, Version of Record 24 December 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.114162