Time series prediction can be generalized as a process that extracts useful
information from historical records and then determines future values. Learning
long-range dependencies that are embedded in time series is often an obstacle
for most algorithms, whereas Long Short-Term Memory (LSTM) solutions, as a
specific kind of scheme in deep learning, promise to effectively overcome the
problem. In this article, we first give a brief introduction to the structure
and forward propagation mechanism of the LSTM model. Then, aiming at reducing
the considerable computing cost of LSTM, we put forward the Random Connectivity
LSTM (RCLSTM) model and test it by predicting traffic and user mobility in
telecommunication networks. Compared to LSTM, RCLSTM is formed via stochastic
connectivity between neurons, which achieves a significant breakthrough in the
architecture formation of neural networks. In this way, the RCLSTM model
exhibits a certain level of sparsity, which leads to an appealing decrease in
the computational complexity and makes the RCLSTM model become more applicable
in latency-stringent application scenarios. In the field of telecommunication
networks, the prediction of traffic series and mobility traces could directly
benefit from this improvement as we further demonstrate that the prediction
accuracy of RCLSTM is comparable to that of the conventional LSTM no matter how
we change the number of training samples or the length of input sequences.

本文介绍 LSTM 模型的结构和前向传播机制，提出了 Random Connectivity LSTM（RCLSTM）模型，并通过在电信网络交通预测和用户移动性中的应用证明了 RCLSTM 模型可实现相同的预测准确性，同时具有计算复杂度更低的优点