# Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network  
[PERSON]  
1 School of Software, Yunnan University, Kunming 650504, China; [EMAIL_ADDRESS] (L.L.);  
[EMAIL_ADDRESS] (C.X.); [EMAIL_ADDRESS] (B.Y.); [EMAIL_ADDRESS] (Q.L.)1 School of Software, Yunnan University, Kunming 650504, China; [EMAIL_ADDRESS] (L.L.);  
[EMAIL_ADDRESS] (C.X.); [EMAIL_ADDRESS] (B.Y.); [EMAIL_ADDRESS] (Q.L.)1 School of Software, Yunnan University, Kunming 650504, China; liulinan@mail.  
However, cellular network traffic changes have elusive rules, and the variation in traffic in a particular region is strongly correlated with many external factors, such as business, location, time, and user lifestyle. To extract more effectively the changing characteristics of cellular network traffic, many related studies have been carried out. The existing methods can be divided into two types: statistical or probabilistic methods and machine learning methods.  
For the first kind of methods, this includes the autoregressive integrated moving average (ARIMA) [9; 10], \(\alpha\)-stable distribution [11], and covariance function [12]. In the traffic prediction problem, these methods have comprehensively studied the characteristics of cellular networks and have shown that changes in communication traffic have both temporal autocorrelation and spatial autocorrelation. However, as the communication modes of cellular networks become more complex and subject to many external factors, these traditional linear statistical methods are not suitable for current communication traffic prediction problems.  
With the development of artificial intelligence technology, machine learning methods have been widely used in industry and have also been used for cellular network traffic prediction in recent years [13]. Early researchers proposed using linear regression [14] and SVM regression [15] to predict cellular traffic. Many studies have also proposed methods for traffic prediction based on deep learning. In [16], the authors proposed a deep learning based prediction method to simulate the long term dependence of cellular network traffic in 2017. The method mainly uses self-encoded depth models and long short term memory cells (LSTM) for space-time modeling. However, the processing of self-encoding will lose some of the original information, and the ability to extract spatial features needs to be improved. In 2018, [PERSON] et al. [17] proposed a cellular traffic prediction method based on a convolutional neural network (STDenseNet); however, this method did not consider the impact of external conditions on traffic prediction, and the traditional convolution method had a limited effect on the complex spatial characteristics of cellular traffic.  
Motivated by the aforementioned problems, based on STDenseNet, this work proposes a new hybrid spatiotemporal network (HSTNet). First, the deformable convolution unit is used in the model to improve the ability to extract complex spatial features. Then, time characteristics are introduced to enhance the accuracy of traffic prediction. Finally, an attention mechanism based on traffic history data is proposed to further enhance the robustness of the model.