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

arXiv: https://arxiv.org/abs/1810.10161 · HTML: https://mlsanity.com/paper/1810.10161/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2018.

## Built on by (most cited first, 30 of 39)

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- [TS-OOD: Evaluating Time-Series Out-of-Distribution Detection and Prospective Directions for Progress](https://mlsanity.com/paper/2502.15901/) (2025)
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- [Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learning](https://mlsanity.com/paper/2310.14456/) (2023)
- [Context-Aware Prediction of User Engagement on Online Social Platforms](https://mlsanity.com/paper/2310.14533/) (2023)
- [MTSA-SNN: A Multi-modal Time Series Analysis Model Based on Spiking Neural Network](https://mlsanity.com/paper/2402.05423/) (2024)
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- [SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2505.02655/) (2025)
- [Temporal Variational Implicit Neural Representations](https://mlsanity.com/paper/2506.01544/) (2025)
- [A Comparative Analysis of Traditional and Deep Learning Time Series Architectures for Influenza A Infectious Disease Forecasting](https://mlsanity.com/paper/2507.19515/) (2025)

## Abstract (arXiv)

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.