@Article{jqc.2021.016346,
AUTHOR = {Wenxiao Wang, Xiaoyu Li, Yin Ding, Feizhou Wu, Shan Yang},
TITLE = {A Prediction Method of Trend-Type Capacity Index Based on Recurrent  Neural Network},
JOURNAL = {Journal of Quantum Computing},
VOLUME = {3},
YEAR = {2021},
NUMBER = {1},
PAGES = {25--33},
URL = {http://www.techscience.com/jqc/v3n1/42589},
ISSN = {2579-0145},
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.},
DOI = {10.32604/jqc.2021.016346}
}