A new approach for optimal offline time-series segmentation with error bound guarantee
作者：
Highlights：
• An optimal offline time-series segmentation with error bound guarantee is proposed (OSFS method).
• The OSFS method is based on finding the shortest path in a directed graph.
• In order to reduce the computational time, the feasible space method (FS), proposed by Liu, is used.
• A new performance measure to evaluate the performance of heuristic and metaheuristic methods has also been proposed.
• These results demonstrate that the L-infinity norm produces better results than the L∞-norm.
摘要
•An optimal offline time-series segmentation with error bound guarantee is proposed (OSFS method).•The OSFS method is based on finding the shortest path in a directed graph.•In order to reduce the computational time, the feasible space method (FS), proposed by Liu, is used.•A new performance measure to evaluate the performance of heuristic and metaheuristic methods has also been proposed.•These results demonstrate that the L-infinity norm produces better results than the L∞-norm.
论文关键词：Data representation,Optimal time series segmentation,Error bound guarantee,L∞-norm
论文评审过程：Received 8 June 2020, Revised 8 February 2021, Accepted 20 February 2021, Available online 23 February 2021, Version of Record 4 March 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.107917
