基于AR与DNN联合模型的地理传感器时间序列预测

Geo-sensory Time Series Prediction Based on Joint Model of Auto Regression and Deep Neural Network

地理传感器时间序列具有复杂动态的语义时空相关性和地理时空相关性。尽管已经开发了各种深度学习模型用于时间序列预测,但很少有模型能专注于捕捉地理传感器时间序列内的多类型时空相关性。此外,同时预测多个传感器在未来某一时间步的值非常具有挑战性。为了解决上述问题,提出了一种自回归模型与深度神经网络的联合模型(Joint model of Autoregression and Deep Neural Network,J-ARDNN),用于处理地理传感器时间序列的多目标预测任务。在该模型中,空间模块用于捕捉不同序列间多类型空间的相关性,时间模块采用时间卷积网络来提取单个序列内的时间依赖关系。此外,还引入自回归模型来提高预测模型的鲁棒性。为了验证J-ARDNN模型的有效性和优越性,在不同领域的真实时间序列数据集上进行了充分的实验,结果表明,J-ARDNN模型的预测性能优于对比方法。

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.