CDN带宽异常值的预测和准确告警一直是网络运营的重点和难点，为此在时间序列LSTM （long short term memory network）基础之上，提出并实现了一套新的算法框架——局部加权回归串行LSTM.框架采用时序插值采样方法构造数据集，局部加权算法融入最小二乘回归拟合模型进行初始预测，预测结果串行LSTM时序模型进行最终带宽异常值预测，使用4sigma方法判断某时刻带宽是否为异常，并按等级标准发出异常告警.实验结果显示该模型实现了带宽异常值的预判及告警.
The prediction and accurate warning of CDN bandwidth outliers have always been the focus and difficulty of network operation. For this reason, the study proposes and implements a new algorithm framework, the serial LSTM (long short-term memory) network with locally weighted regression, based on the LSTM network with time series. The framework uses the time-series interpolation sampling method to construct the data set, and the local weighting algorithm is integrated into the fitting model based on least square regression for initial prediction. The prediction result is serialized with the LSTM time series model for the final bandwidth outlier prediction. The 4sigma method is used to determine whether the bandwidth is abnormal at a certain moment, and an abnormal alarm is issued according to the grade standard. The experimental results show that the model is effective for the prediction and alarm of bandwidth outliers.