## 2 Related Works  
Many cities are facing the challenges brought by rapid urbanization [12]. In recent years, research on smart cities has attracted extensive attention. [PERSON] et al. mentioned that urban flow analysis is an important research content of smart city construction, in which urban flow model analysis focuses on the continuous state of urban flow. How to mine, store and reuse traffic patterns from urban multi-source heterogeneous big data is a challenge. Therefore, they proposed a regional flow pattern knowledge mining network to mine and store urban flow patterns. [PERSON] et al. [13] indicated that wireless traffic can be used for smart city research. They pointed out that smart cities need to make full use of information technology to respond intelligently to all needs, including networks and urban services. They proposed a differential evolution back propagation (DE-BP) neural network traffic prediction model suitable for intelligent city network to predict network traffic. Similarly, [PERSON] et al. [14] transformed ubiquitous mobile phone data into interpretable human mobility patterns, taking Singapore as an example. Through the development of data mining pipeline, the spatial distribution of residents' travel patterns in different urban areas was quantified. The ultimate goal was to help planners effectively acquire urban knowledge from big data to target specific urban areas to improve future infrastructure and service planning. [PERSON] [15] pointed out that in order to develop a smart city, it is necessary to integrate all components of the city into a system. As a technology, urban computing can solve the complexity of providing appropriate services to citizens through different urban departments or systems, which promotes this. Similar to some other urban computing studies [13; 14; 16], we also use wireless network data in urban computing research. In the process of data processing, data mining based on a decomposition and integration system is proposed. EMD is used to decompose the original data. EMD is a technique  
Figure 2: Structure of DIC-ST.  
of signal feature extraction, which can decompose time series data into finer-grained components [17]. As a timing processing technology, EMD has been widely used. In [18], aiming at the research of meteorological index prediction, the authors put forward three prediction models: a hybrid prediction model based on residual prediction, a hybrid prediction model based on EMD and a hybrid prediction model based on EMD and residual prediction. The authors tested three models. The experimental results showed that prediction accuracy of three models was significantly improved compared with the traditional model. The third model is a hybrid prediction method based on EMD and residual prediction, and its prediction performance was the best. The reason was that EMD makes the original sequence stable, and prediction performance of this model on stable data is generally good. [PERSON] et al. [19] proposed a hybrid short-term wind speed prediction method based on EMD and random vector function chain network. Firstly, the chaotic historical wind speed data is decomposed into several IMFs by EMD. These IMFs are used in the proposed prediction model. Finally, the effectiveness was confirmed. In [20], motivation of the research is that IMF allows to calculate the meaningful Hilbert transform of non-stationary data, from which the instantaneous time-frequency representation can be derived. Our spatiotemporal intrinsic mode decomposition method uses spatial correlation to extend the extraction of IMF from one-dimensional signals to multi-dimensional signals. It can be seen that most studies put the subsequences decomposed of EMD into different models. In this paper, after EMD decomposition, we integrated and extracted three components in the timing. This step ensured that the characteristics of each subsequence are consistent, so that the same prediction model can be used. At the same time, the complexity was greatly reduced.