| dc.contributor.author | DONG Hongbin, HAN Shuang, FU Qiang | |
| dc.contributor.other | College of Computer Science and Technology,Harbin Engineering University,Harbin 150001,China | |
| dc.date.accessioned | 2025-08-27T02:35:33Z | |
| dc.date.accessioned | 2025-10-08T08:22:40Z | |
| dc.date.available | 2025-10-08T08:22:40Z | |
| dc.date.issued | 01-11-2023 | |
| dc.identifier.uri | http://digilib.fisipol.ugm.ac.id/repo/handle/15717717/35657 | |
| dc.description.abstract | Geo-sensory time series contain complex and dynamic semantic spatio-temporal correlations and geographic spatio-temporal correlations.Although a variety of existing deep learning models have been developed for time series prediction,few of them focus on capturing multi-type of spatial-temporal correlations within geo-sensory time series.In addition,it is challenging to si-multaneously predict the future values of multiple sensors at a certain time step.To address these issues and challenges,this paper proposes a joint model of autoregression and deep neural network(J-ARDNN) to achieve the multi-objective prediction task of geo-sensory time series.In this model,the spatial module is proposed to capture the multi-type spatial correlations between diffe-rent series,the temporal module introduces the temporal convolutional network to extract the temporal dependencies within a single series.Moreover,the autoregression model is introduced to improve the robustness of the J-ARDNN prediction model.To prove the superiority and effectiveness of the J-ARDNN model,the proposed model is evaluated in three real-world datasets from different fields.Experimental results show that the proposed model can achieve better prediction performance than state-of-the-art contrast models. | |
| dc.language.iso | ZH | |
| dc.publisher | Editorial office of Computer Science | |
| dc.subject.lcc | Computer software | |
| dc.title | Geo-sensory Time Series Prediction Based on Joint Model of Auto Regression and Deep NeuralNetwork | |
| dc.type | Article | |
| dc.description.keywords | geo-sensory time series|multi-objective prediction|spatio-temporal correlation|autoregression model|deep neural network | |
| dc.description.pages | 41-48 | |
| dc.description.doi | 10.11896/jsjkx.230500231 | |
| dc.title.journal | Jisuanji kexue | |
| dc.identifier.oai | 42ed7ebe6bb94969b7c955f3c07e3dc2 | |
| dc.journal.info | Volume 50, Issue 11 | |
