| dc.description.abstract | With the fast development of the fifth-generation (5G) and deep learning (DL), the demand for data transmission capacity is getting more and more. Analyzing and forecasting based on big data is a tendency to manage cell sites intelligently. To have full use of ML, the powerful ML model: 3D CNN, RNN, and CNN-RNN are very reliable. We have already grasped the technique of predicting mobile internet traffic based on one kind of data, internet traffic, in our previous work cite{previous_paper}. However, internet traffic is not only affected by one factor. It may be due to external conditions, which in turn affects the goals we want to predict. In this work, we use internet traffic, periodic data, weather, news and social data from the city of Milan during November and December in 2013 to catch the relationship between multi-data with a deep multi-modal learning model, called Multi-modal CNN-RNN (MMCR), bring on more precise forecasting than only one kind of data. Combine different data through a fused approach, so that relevant data can help us to predict the target. We also use the learning method to adjust the data at the current time through the deep learning model. The experimental results show that using data that is helpful for network traffic can improve prediction accuracy. And we also compare with the architecture designed by other work. Our method can also get good results. | en_US |