# Network Traffic Prediction Incorporating Prior Knowledge for an Intelligent Network
## 2 Related Work  
Existing time-series forecasting models can be divided into two main categories: linear forecasting models and non-linear forecasting models. Of these, linear forecasting uses traditional statistical methods and can be applied to the prediction of smooth series. Common linear forecasting models are the historical average (HA) [3], autoregressive moving average [4] (ARMA), and autoregressive integrated moving average model (ARIMA) models [5]. The historical average (HA) model uses historical averages as predictions [15] while the ARMA model, developed by [PERSON] et al., can be used to predict the feasibility of exceeding the threshold of network traffic. [PERSON] et al. [16] decomposed the network traffic data into linear and non-linear components based on a discrete wavelet transform (DWT), and then used the autoregressive integrated moving average (ARIMA) for the prediction of non-linear components. At present, however, the burstiness of smart network traffic is high and traditional models, such as Poisson distribution modeling, are only suitable for predicting small network traffic sequences, being unable to effectively extract the characteristics of network traffic at different scales in different times. Thus, they cannot meet the characteristics of smart network traffic. With the development of artificial intelligence, many machine learning and deep learning models have been used to predict network traffic, and such non-linear prediction models have shown good prediction results on non-stationary sequences. For example, [PERSON] et al. [17] proposed the use of wavelet neural networks and artificial neural networks to predict decomposed traffic sequences; however, this approach relies on the selection of wavelet basis functions and the initialization of parameters. [PERSON] et al. [18] proposed a multi-stage prediction model using the grey wolf optimization algorithm and a support vector machine (GWO-SVR), but this approach generally relies on the selection of a kernel function. Extreme learning machine (ELM) [19] and ELM combined with a decomposed fruit-fly optimization algorithm (FOA-ELM) have been used to predict the low- and high-frequency components after traffic decomposition; however, ELM models are generally less stable and improvement of their accuracy depends on an increase in the number of hidden nodes in the network. [PERSON] et al. proposed a long short-term memory (LSTM) model to predict network traffic sequences. LSTM is a variant of RNN, which can overcome the disadvantage of gradient disappearance inherent to RNN models, and which has exhibited an efficient non-linear time-series modeling capability; however, as the length of the prediction sequence increases, it is difficult for a single LSTM network to converge to the global optimum, and the learning process of deep learning algorithms is opaque and less interpretable, compared to traditional statistical models. [PERSON] et al. [13] proposed the prediction of network traffic with Transformer. However, such deep learning models lack interpretability, and the Transformer is poor at establishing long-term dependency capture when long time series need to be predicted. At present, there are two mainstream algorithms for fusing prior knowledge: [PERSON]. [20] proposed the model-driven approach using prior knowledge for pre-processing to improve machine learning algorithms; however, this approach is overly reliant on artificially determined hyper-parameters and is prone to missing information during conversion. It is not wise to completely disregard prior knowledge when extracting features from deep learning models. [PERSON] [21] proposed the model-driven approach using prior knowledge as the intermediate structure in deep networks [22], which can effectively fuse prior knowledge and data by using the prior knowledge to change part of the structure of the network.