Name
Abstract | ||
|---|---|---|
•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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.patcog.2018.10.021 | Pattern Recognition |
Keywords | Field | DocType |
Internet of things (IoTs),Significant societal events,Geosensor data,Forecasting,Tensor decomposition | Spatial correlation,Sliding window protocol,Tensor,Pattern recognition,Tensor rank,Correlation,Artificial intelligence,Mathematics,Tensor decomposition | Journal |
Volume | Issue | ISSN |
88 | 1 | 0031-3203 |
Citations | PageRank | References |
1 | 0.35 | 28 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lihua Zhou | 1 | 18 | 7.71 |
| Guowang Du | 2 | 1 | 1.36 |
| Ruxin Wang | 3 | 228 | 18.13 |
| Dapeng Tao | 4 | 1115 | 61.57 |
| Lizhen Wang | 5 | 153 | 26.16 |
| jun cheng | 6 | 851 | 69.84 |
| Jing Wang | 7 | 1 | 0.35 |