基于循环神经网络的电信行业容量数据预测方法

在电信运维的容量预测过程中，存在容量指标和部署业务种类繁多的问题。现有研究未考虑指标数据类型的差异，对所有类型的数据使用同种预测方法，使得预测效果参差不齐。为了提升指标预测效率，提出一种指标数据类型分类方法，利用该方法将数据类型分为趋势型、周期型和不规则型。针对其中的周期型数据预测，提出基于双向循环神经网络（BiRNN）的周期型容量指标预测模型，记作BiRNN-BiLSTM-BI。首先，为分析容量数据的周期特征，提出一种忙闲分布分析算法；其次，搭建循环神经网络（RNN）模型，该模型包含一层BiRNN和一层双向长短时记忆网络（BiLSTM）；最后，充分利用系统忙闲分布信息，对BiRNN输出的结果进行优化。与传统的三次指数平滑、差分自回归移动平均（ARIMA）模型和反向传播（BP）神经网络模型进行比较的实验结果表明，在统一日志数据集和分布式缓存数据集上，提出的BiRNN-BiLSTM-BI模型的均方误差（MSE）分别比对比模型中表现最优的模型降低了15.16%和45.67%，可见预测准确率得到了很大程度的提升。

Prediction method of capacity data in telecom industry based on recurrent neural network

In the capacity prediction process of telecom operation and maintenance, there are problems of too many capacity indicators and deployed business classes. Most of the existing researches do not consider the difference of indicator data types, and use the same prediction method for all types of data, which results in both good and bad prediction effects. In order to improve the efficiency of indicator prediction, a classification method of data type was proposed, and the data types were divided into trend type, periodic type and irregular type. Aiming at the prediction of periodical data, a periodic capacity indicator prediction model based on Bi-directional Recurrent Neural Network (BiRNN), called BiRNN-BiLSTM-BI, was proposed. Firstly, In order to analyze the periodic characteristics of capacity data, a busy and idle distribution analysis algorithm was proposed. Secondly, a Recurrent Neural Network (RNN) model was built, which included a layer of BiRNN and a layer of Bi-directional Long Short-Term Memory network (BiLSTM). Finally, the output of BiRNN was optimized by the system's busy and idle distribution information. Experimental results compared with the best one among Holt-Winters, AutoRregressive Integrated Moving Average (ARIMA) model and Back Propagation (BP) neural network model show that, the proposed BiRNN-BiLSTM-BI model has the Mean Square Error (MSE) reduced by 15.16% and 45.67% on the unified log dataset and the distributed cache service dataset respectively, showing that the prediction accuracy is greatly improved.