Abstract: Web traffic prediction has always been a hot issue in data center networks, which is of great significance for improving the quality of network services. Due to the complex characteristics of web traffic such as non-linearity, autocorrelation, and periodicity, it is very challenging to accurately predict it. In order to fully mine the predictable information of web traffic and make the prediction model fully interpretable and configurable, this paper proposes a combined prediction model based on Prophet and deep autoregression (DeepAR). Among them, Prophet is an additive model based on time series decomposition, which models the trend, seasonal period, and holiday information of Web traffic. At the same time, the autoregressive information implied by the Prophet residual is modeled using the DeepAR model based on probability prediction, and the long-term and short-term dependencies are captured to reduce the variance of the Prophet residual and fully capture the autoregressive information of web traffic. In this paper, the verification experiments are carried out on the real Web traffic dataset, and the results show that the evaluation indicators of RMSE and MAE are better than the comparative models, which verifies the effectiveness of the combined model.

Key words: time series, web traffic forecasting, prophet model, deep learning, auto-regression