Deep Learning with Long Short-Term Memory for Time Series Prediction

Yuxiu Hua, Zhifeng Zhao, Rongpeng Li, Xianfu Chen, Zhiming Liu, Honggang Zhang

    Research output: Contribution to journalArticleScientificpeer-review

    321 Citations (Scopus)


    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 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 LSTM. Then, aiming at reducing the considerable computing cost of LSTM, we put forward a RCLSTM model by introducing stochastic connectivity to conventional LSTM neurons. Therefore, RCLSTM exhibits a certain level of sparsity and leads to a decrease in computational complexity. In the field of telecommunication networks, the prediction of traffic and user mobility could directly benefit from this improvement as we leverage a realistic dataset to show that for RCLSTM, the prediction performance comparable to LSTM is available, whereas considerably less computing time is required. We strongly argue that RCLSTM is more competent than LSTM in latency-stringent or power-constrained application scenarios.

    Original languageEnglish
    Article number8663965
    Pages (from-to)114-119
    JournalIEEE Communications Magazine
    Issue number6
    Publication statusPublished - 1 Jun 2019
    MoE publication typeA1 Journal article-refereed


    This work was supported in part by the National Natural Science Foundation of China (Nos. 61701439 and 61731002), and the Zhejiang Key Research and Development Plan (No. 2018C03056).


    • logic gates
    • computer architecture
    • biological neural networks
    • computational modeling
    • deep learning


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