Vehicle operating state anomaly detection and results virtual reality interpretation

作者：

Highlights：

• Develop ARIMA based time-series anomaly detection approach to handle vehicle operating status identification.

• Use immersive Virtual Reality to interpret obtained results.

• Use large scale vehicle operating time series dataset to validate proposed approach.

摘要

•Develop ARIMA based time-series anomaly detection approach to handle vehicle operating status identification.•Use immersive Virtual Reality to interpret obtained results.•Use large scale vehicle operating time series dataset to validate proposed approach.

论文关键词：Multi-channel time series data,Anomaly detection,Autoregressive integrated moving average approach,Immersive virtual reality,Data visualization

论文评审过程：Received 23 July 2020, Revised 26 December 2020, Accepted 18 March 2021, Available online 26 March 2021, Version of Record 19 April 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.114928