## 1 Introduction
Due to the complexity and randomness of cellular network data, wireless network traffic data can not be well analyzed by the traditional model. However, although the traffic data in wireless network is complex, it has its own unique characteristics, such as tidal, seasonality, etc. [9]. We proposed a method to extract trend component, periodic component and essential component from complex time series. Then, according to the idea of hybrid model, the decomposition and integration the prediction scheme is adopted to predict three different components, respectively, and finally three prediction results are combined into the final prediction result. In the prediction of three components, we introduced causal data mining. Different from the traditional temporal causal analysis, we increased the depth of causal mining, not for causal verification of time series, but for different components of same time series. Final results showed that in urban wireless networks, the causality correlation between base stations is different in the trend component, periodic component and essential component. This verification result is important for network management and construction. This deep data mining can not only enhance the accuracy of prediction, but also play a role in urban planning, citywide network construction and resource planning. Causal structure learning was introduced to find the relationship between different time series, and the purpose is to use the relationship between different time series for prediction performance enhancement. The effectiveness of causality for time series prediction has been confirmed in some studies [10; 11]. In this paper, we proposed deep causal mining for different components in time series. In the research of smart city, big data temporal spatial analysis is an important part. We decomposed time series data into three components. Each component plays a different role in urban computing. The trend component represents the overall number of users in the region. The research on the trend component can be used in base station construction, urban planning and so on. The periodic component symbolizes the law of population flow and can be used for wireless network management and traffic information monitoring. The essential component is the key to ensure the quality of user service. We establish a temporal-spatial model for each component and introduce causal structure learning into the model. For different components, the correlation between regions in the city can be found, which can be used for data prediction and analysis in urban computing. As mentioned earlier, decomposition integration system and causal mining are combined in this paper. System structure was shown in Figure 2. Traffic data of each base station in the region is decomposed into multiple subsequences. The first integration step is to clustering three components of data. Causal structure learning was used to deal with each component of different regions separately. After causal structure for different components was obtained, this structure can be used for time series prediction. The second integration part is to combine the three prediction results into the final prediction result. A spatial-temporal time series prediction based on decomposition and integration system with causal structure learning (DIC-ST) was proposed. Actual data was used to verify the algorithm and compare with other commonly used algorithms. The main contributions of this paper are as follows:  
1. Based on empirical mode decomposition (EMD), a time series clustering method based on spectrum information and information entropy was proposed. This method can extract the cellular network traffic data into three components: periodic component, trend component and essential component. Figure 1: Remote sensing map of data collection area. 2. We propose a deep causal mining method for time series data. The traditional time series causal analysis was directly applied to multiple time series, ignoring the different components of time series.