The anomaly detection for communication networks is significant for improve the quality of communication services and network reliability. However, traditional communication monitoring methods lack proactive monitoring and real-time alerts and the prediction effect of a single machine learning model on communication data containing multiple features is not ideal. To solve the problem, A prediction-then-detection anomaly detection method was proposed, and quantitative assessment of network anomalies was developed. Specifically, anomaly-free data was obtained by eliminating outliers, and the long short-term memory (LSTM) and autoregressive integral moving average (ARIMA) were combined via residual weighting to predict the future state of the key performance indicators (KPI) without outliers. Anomalies were identified using the error comparison between the prediction and actual values, and the network condition was quantified using the scoring method. It is observed that the proposed LSTM-ARIMA hybrid model has better prediction effect, which can well represent the performance of KPIs of the future state, and the prediction-then-detection anomaly detection method has excellent performance on both precision and recall.