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

By Yuxiu Hua, Zhifeng Zhao, Rongpeng Li, Xianfu Chen, Zhiming Liu, Honggang Zhang. IEEE Communications Magazine, 2018

554 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Building and Construction, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2582c54766c387f7ee80a21b91a7f5bae262896a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Fei Ye
2. Adrian G. Bors
3. Tongtong Su
4. Huazhi Sun
5. Yabo Li
6. Jinqi Zhu
7. Sheng Wang

In Signal Processing

1. Fei Ye
2. Adrian G. Bors

Citations in the corpus

• BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset (2020, methodology): “The forward propagation [40], [41] is conducted from BLSTM layer to attention layer.”
• Continual Variational Autoencoder Learning via Online Cooperative Memorization (2022, background): “Knowing both the past and future information was shown to improve time series prediction [22] and would be helpful for the sample selection.”
• Parallel spatio-temporal attention-based TCN for multivariate time series prediction (2021, background): “Presently, recurrent neural networks (RNNs) are the go-to approach for multivariate time series prediction [15], [16].”