Dynamic time warp-based clustering: Application of machine learning algorithms to simulation input modelling

作者：

Highlights：

• A novel approach based on exploratory machine learning to generate system behaviours.

• The first step in planning complex stochastic systems is to model the input processes.

• Grouping input data into clusters provides more targeted analysis.

• Well-grounded balance between fidelity and tractability compared with other methods.

摘要

•A novel approach based on exploratory machine learning to generate system behaviours.•The first step in planning complex stochastic systems is to model the input processes.•Grouping input data into clusters provides more targeted analysis.•Well-grounded balance between fidelity and tractability compared with other methods.

论文关键词：Complex socio-technical system,Input modelling,Unsupervised machine learning,Discrete event simulation,Dynamic time warping

论文评审过程：Received 28 June 2020, Revised 7 July 2021, Accepted 27 July 2021, Available online 31 July 2021, Version of Record 12 August 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115684