A Network Traffic Prediction Method for AIOps Based on TDA and Attention GRU

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.

Automated summary

Fault early warning is a challenge in operation and maintenance, and this research proposes a targeted solution by combining spatiotemporal characteristics and topological characteristics in the TCAG model to improve fault prediction accuracy.