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.