## 1 Introduction  
With the development of urbanization, a large number of people gather in the city, while cities provide people with an environment for work, study and life; additionally, problems such as traffic congestion and environmental degradation appear. Traditional urban management schemes can no longer cope with this situation. In this context, the concept of smart city came into being. Urban computing is a core technology in smart city construction. Urban computing is a new cross field of urban planning, transportation, energy, environment, economy and sociology based on computer science. The task of urban computing is to first perceive and obtain all kinds of big data generated in the city, and then analyze, process and display big data by using efficient data management technology, advanced algorithms and novel visualization method, so as to solve many problems and challenges existing in the city, such as traffic congestion, poor network quality, backward planning and so on. Urban computing based on mobile communication data is an important means to promote urban intelligence. Compared with other data sources, the advantage of wireless network data is to increase non-public transport users information, such as private cars, bicycles in passenger flow analysis or real-time traffic analysis. Urban planning can also be carried out in a more accurate and comprehensive way. Most importantly, with the increasing number of mobile users, mobile communication has become an indispensable part of people's life. The accurate analysis of wireless network data is the basis for providing users with high-quality multimedia services, so as to ensure the requirements of users for high precision and low delay in different application scenarios such as automatic driving, telemedicine, AR, VR, etc. Urban computing is an emerging discipline to improve the quality of people's life in the city [1]. Wireless network data is divided into signaling data and billing data. Signaling data can be divided into call detail record (CDR) and signal data, including user ID, base station ID, longitude and latitude, etc. The billing data records the user's service start and end time, traffic volume and other information. The large amount of mobile data and long observation cycle can be used to analyze the people footprint in cities on an unprecedented scale [2; 3]. In [4], authors described the research progress of wireless traffic in crowd mobility, geographical zoning, urban planning, development and security and privacy. In this paper, we use real wireless network data from an operator in China, and the data was collected by 22 base stations in an area. Figure 1 showed the area where 22 base stations are located. This data collects the traffic information of each base station in the region for 100 days. The joint analysis of cellular traffic prediction and remote sensing map as shown in Figure 1 can help us accurately sort out mobility of the city. The combination of wireless network data and remote sensing data has many advantages; for traffic prediction, because wireless network data is often the statistical value of regional data, these data are not based on the road as vehicular traffic flow data. Combined with remote sensing map, traffic congestion can be quickly located according to wireless network data. For wireless network optimization, traditional network management requires manual positioning of areas with poor network quality. Using remote sensing data for network management can accurately optimize the network in a visual way. Wireless network data often appear in big data analysis technology in a form of time series. Time series prediction is one of the important research topics in the field of data analysis [5]. Time series prediction research involves various fields, such as economy, society, energy, environment, climate and other research fields [6; 7; 8]. There are three methods for time series analysis and prediction: traditional statistical models, artificial intelligence models and hybrid models.