A tensor framework for geosensor data forecasting of significant societal events. (April 2019)

• Record Type: Journal Article
• Authors: Zhou, Lihua; Du, Guowang; Wang, Ruxin; Tao, Dapeng; Wang, Lizhen; Cheng, Jun; Wang, Jing
• Is Part Of: Pattern recognition. Volume 88(2019:Apr.)
• Page Start: 27
• Page End: 37
• Publication Date: 2019-04
• Subjects: Internet of things (IoTs) -- Significant societal events -- Geosensor data -- Forecasting -- Tensor decomposition; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.10.021
• Languages: English
• ISSNs: 0031-3203

Abstract

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. Abstract: Geosensor data forecasting has high practical value in government affairs such as prompt response and decision making. However, the spatial correlation across distinct sites and the temporal correlation within each site pose challenges to accurate forecasting. In this paper, a geosensor data forecasting tensor framework for significant societal events is proposed. Specifically, a tensor pattern is used to model the geosensor data, based on which a tensor decomposition algorithm is then developed to estimate future values of geosensor data. The proposed approach not only combines and utilizes the multi-mode correlations, but also well extracts the underlying factors in each mode of tensor and mines the multi-dimensional structures of geosensor data. In addition, a rank increasing strategy is used to determine tensor rank automatically, 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.