A shape-based adaptive segmentation of time-series using particle swarm optimization

作者：

Highlights：

• A novel method for time-series’ segmentation based on Particle Swarm Optimization (PSO) is proposed.

• The proposed approach is highly adaptive to time-series’ shape and characteristics.

• The proposed Adaptive Particle Swarm Optimization Segmentation (APSOS) is tested on various datasets to verify its effectiveness and efficiency for the goal of segmentation.

• Experiments and the results indicate that the proposed algorithm outperforms other methods in the area of segmentation.

摘要

•A novel method for time-series’ segmentation based on Particle Swarm Optimization (PSO) is proposed.•The proposed approach is highly adaptive to time-series’ shape and characteristics.•The proposed Adaptive Particle Swarm Optimization Segmentation (APSOS) is tested on various datasets to verify its effectiveness and efficiency for the goal of segmentation.•Experiments and the results indicate that the proposed algorithm outperforms other methods in the area of segmentation.

论文关键词：

论文评审过程：Received 24 May 2016, Revised 13 March 2017, Accepted 19 March 2017, Available online 21 March 2017, Version of Record 22 March 2017.

论文官网地址：https://doi.org/10.1016/j.is.2017.03.004