A Prediction Method of Trend-Type Capacity Index Based on Recurrent Neural Network

Abstract

Due to the increase in the types of business and equipment in telecommunications companies, the performance index data collected in the operation and maintenance process varies greatly. The diversity of index data makes it very difficult to perform high-precision capacity prediction. In order to improve the forecasting efficiency of related indexes, this paper designs a classification method of capacity index data, which divides the capacity index data into trend type, periodic type and irregular type. Then for the prediction of trend data, it proposes a capacity index prediction model based on Recurrent Neural Network (RNN), denoted as RNN-LSTM-LSTM. This model includes a basic RNN, two Long Short-Term Memory (LSTM) networks and two Fully Connected layers. The experimental results show that, compared with the traditional HoltWinters, Autoregressive Integrated Moving Average (ARIMA) and Back Propagation (BP) neural network prediction model, the mean square error (MSE) of the proposed RNN-LSTM-LSTM model are reduced by 11.82% and 20.34% on the order storage and data migration, which has greatly improved the efficiency of trend-type capacity index prediction.