# A Network Traffic Prediction Method for AIOps Based on TDA and Attention GRU
###### Abstract  
Fault early warning is a challenge in the field of operation and maintenance. Considering the improvement of accuracy and real-time standards, as well as the explosive growth of operation and maintenance data, traditional manual experience and static threshold can no longer meet the production requirements. This research fully digs into the difficulties in fault early warning and provides targeted solutions in several aspects, such as difficulty in feature extraction, insufficient prediction accuracy, and difficulty in determining alarm threshold. The TCAG model proposed in this paper creatively combines the spatiotemporal characteristics and topological characteristics of specific time series data to apply to time series prediction and gives the recommended dynamic threshold interval for fault early warning according to the prediction value. A data comparison experiment of a core router of Ningxia Electric Power Co., Ltd. shows that the combination of topological data analysis (TDA) and convolutional neural network (CNN) enables the TCAG model to obtain superior feature extraction capability, and the support of the attention mechanism improves the prediction accuracy of the TCAG model compared to the benchmark models. time series prediction; failure warning; attention GRU; TDA; CNN; AIOps +
Footnote †: journal: Applied sciences  
super parameters, long training times, and the reliance on more GPU resources. Therefore, most of the current industrial application models are a compromise between model complexity, resource consumption, and time consumption. 3. The traditional automatic operation and maintenance mode is solidified and depends more on manual experience. At this stage, AIOps also needs to set rules manually in many scenarios. Therefore, how to use the algorithm model to automatically learn various rules to reduce manual intervention is not only the continuous goal of AIOps but is also an urgent problem to be solved. Based on the above challenges, aiming at the problem of fault early warning in AIOps, this paper proposes a set of intelligent operation and maintenance architecture. The architecture can predict the value of the future period according to the historical data of information equipment and set the dynamic threshold interval based on predicted value to eliminate the dependence on manual experience and overcome the problem that the traditional threshold setting methods are difficult to deal with frequent scene changes. Specifically, the architecture first uses the convolutional neural network to extract spatiotemporal features in time series and then creatively applies topological data analysis to time series to extract topological features in data. Finally, the neural network model is trained using the extracted spatiotemporal and topological features to obtain the predicted value and dynamic threshold interval. To deal with the long-term dependence on time series data, the attention mechanism is applied in the gated cyclic unit (GRU) and is then used as the prediction model. The main contributions of this paper can be summarized as follows. 1. To solve the problem of fault early warning in AIOps, we propose a novel dynamic threshold-setting mechanism that calculates a threshold interval that automatically adjusts to scene changes based on prediction results and can fully consider the prediction error caused by the increase in step size. 2. Owing to the possible instability and nonlinearity in time series and long-time dependence, the performance of traditional feature extraction methods is not satisfactory. To obtain the most concise and effective feature expression of the data, this study uses a convolutional neural network to extract the spatio-temporal features in the data and uses topological data analysis to extract the topological features in the data. As far as the authors are aware, this is the first time topological data analysis has been applied to research in the field of AIOps. 3.