# Network Traffic Prediction Incorporating Prior Knowledge for an Intelligent Network
###### Abstract  
Network traffic prediction is an important tool for the management and control of IoT, and timely and accurate traffic prediction models play a crucial role in improving the IoT service quality. The degree of burstiness in intelligent network traffic is high, which creates problems for prediction. To address the problem faced by traditional statistical models, which cannot effectively extract traffic features when dealing with inadequate sample data, in addition to the poor interpretability of deep models, this paper proposes a prediction model (fusion prior knowledge network) that incorporates prior knowledge into the neural network training process. The model takes the self-similarity of network traffic as a priori knowledge, incorporates it into the gating mechanism of the long short-term memory neural network, and combines a one-dimensional convolutional neural network with an attention mechanism to extract the temporal features of the traffic sequence. The experiments show that the model can better recover the characteristics of the original data. Compared with the traditional prediction model, the proposed model can better describe the trend of network traffic. In addition, the model produces an interpretable prediction result with an absolute correction factor of 76.4%, which is at least 10% better than the traditional statistical model. network traffic prediction; self-similarity; Hurst exponent; a priori knowledge; intelligent networks 202 historical data). Common network traffic prediction models can be divided into two major categories: linear prediction and non-linear prediction. Linear prediction methods mainly use a polynomial fitting function to approximate the actual network traffic, and the fitting effect is improved when there are a large number of parameter adjustments. The traditional linear forecasting models are the historical average model [3], autoregressive model, autoregressive sliding average (also known as the autoregressive moving average model), and modified models based on these [4; 5]. However, linear prediction methods have difficulty in capturing non-linear features, such as rapid fluctuations, and the time-dependence of network traffic. Non-linear prediction techniques have emerged with the introduction of artificial neural networks [6; 7; 8], for example, data-driven deep learning models, such as convolutional neural networks (CNNs) [9] and recurrent neural networks (RNNs) [10; 11], in addition to machine learning algorithms, such as support vector regression (SVR) [12] and Transformer [13]. Although neural network-based models have demonstrated impressive predictive performances, deep learning models are often treated as black-box models. Compared with traditional statistical models, the learning process of deep learning algorithms is intricate and less interpretable. Existing studies have shown that the network traffic flow has obvious self-similarity [14]. In other words, the local and overall time-series have a certain connection, in which the shape of a part of the series is very similar to the overall series, and the network traffic time-series shows similar abrupt changes at different time scales over a long period. Figure 1 depicts the obvious self-similarity of network traffic. It shows the change in the traffic within a week while the magnified inset shows the change in the traffic within one day, from which it can be seen that the trend of the network traffic change in a week is roughly the same as that in a day; that is to say, the historical traffic data and the data to be predicted have the same characteristics and, thus, this similarity can be used to enhance the accuracy and interpretability of network traffic prediction. To explore the accuracy and interpretability deeply, this paper proposes a network traffic prediction model, named FPK-Net (Fusion Prior Knowledge Network), which incorporates prior knowledge.