A tensor framework for geosensor data forecasting of significant societal events

作者：

Highlights：

• A geosensor data forecasting tensor framework (GDFTF) for significant societal events is proposed.

• A rank increasing strategy and a sliding window strategy is used to improve the prediction accuracy.

• Extensive experimental evaluations illustrate the superiority of our approach compared with the state-of-the-arts.

摘要

•A geosensor data forecasting tensor framework (GDFTF) for significant societal events is proposed.•A rank increasing strategy and a sliding window strategy is used to improve the prediction accuracy.•Extensive experimental evaluations illustrate the superiority of our approach compared with the state-of-the-arts.

论文关键词：Internet of things (IoTs),Significant societal events,Geosensor data,Forecasting,Tensor decomposition

论文评审过程：Received 28 February 2018, Revised 10 August 2018, Accepted 16 October 2018, Available online 25 October 2018, Version of Record 10 November 2018.

论文官网地址：https://doi.org/10.1016/j.patcog.2018.10.021