# Long-Term Mobile Traffic Forecasting Using Deep Spatio-Temporal Neural Networks

arXiv: https://arxiv.org/abs/1712.08083 · HTML: https://mlsanity.com/paper/1712.08083/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2017.

## Built on by (most cited first, 28 of 28)

- [ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2307.00754/) (2023)
- [BACKTIME: Backdoor Attacks on Multivariate Time Series Forecasting](https://mlsanity.com/paper/2410.02195/) (2024)
- [FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecasting](https://mlsanity.com/paper/2212.01209/) (2022)
- [Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Control](https://mlsanity.com/paper/2101.08391/) (2021)
- [LINKs: Large Language Model Integrated Management for 6G Empowered Digital Twin NetworKs](https://mlsanity.com/paper/2412.19811/) (2024)
- [Towards Energy-Aware Federated Traffic Prediction for Cellular Networks](https://mlsanity.com/paper/2309.10645/) (2023)
- [Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting](https://mlsanity.com/paper/2412.04081/) (2024)
- [ST-FiT: Inductive Spatial-Temporal Forecasting with Limited Training Data](https://mlsanity.com/paper/2412.10912/) (2024)
- [Location Leakage in Federated Signal Maps](https://mlsanity.com/paper/2112.03452/) (2021)
- [Graph-Free Learning in Graph-Structured Data: A More Efficient and Accurate Spatiotemporal Learning Perspective](https://mlsanity.com/paper/2301.11742/) (2023)
- [ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting](https://mlsanity.com/paper/2108.05940/) (2021)
- [Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learning](https://mlsanity.com/paper/2310.14456/) (2023)
- [KEDformer:Knowledge Extraction Seasonal Trend Decomposition for Long-term Sequence Prediction](https://mlsanity.com/paper/2412.05421/) (2024)
- [Eyes Will Shut: A Vision-Based Next GPS Location Prediction Model by Reinforcement Learning from Visual Map Feed Back](https://mlsanity.com/paper/2507.18661/) (2025)
- [HiSTM: Hierarchical Spatiotemporal Mamba for Cellular Traffic Forecasting](https://mlsanity.com/paper/2508.09184/) (2025)
- [Vision-LLMs for Spatiotemporal Traffic Forecasting](https://mlsanity.com/paper/2510.11282/) (2025)
- [Deep Reinforcement Learning-Aided RAN Slicing Enforcement for B5G Latency Sensitive Services](https://mlsanity.com/paper/2103.10277/) (2021)
- [A Unified Prediction Framework for Signal Maps](https://mlsanity.com/paper/2202.03679/) (2022)
- [A Dynamic Temporal Self-attention Graph Convolutional Network for Traffic Prediction](https://mlsanity.com/paper/2302.10428/) (2023)
- [An Adaptive Framework for Generalizing Network Traffic Prediction towards Uncertain Environments](https://mlsanity.com/paper/2311.18824/) (2023)
- [Cellular Traffic Prediction Using Online Prediction Algorithms](https://mlsanity.com/paper/2405.05239/) (2024)
- [Breaking the Context Bottleneck on Long Time Series Forecasting](https://mlsanity.com/paper/2412.16572/) (2024)
- [Uncovering Issues in the Radio Access Network by Looking at the Neighbors](https://mlsanity.com/paper/2504.14686/) (2025)
- [Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting](https://mlsanity.com/paper/2507.19513/) (2025)
- [Lightning Prediction under Uncertainty: DeepLight with Hazy Loss](https://mlsanity.com/paper/2508.07428/) (2025)
- [Spatial PDE-aware Selective State-space with Nested Memory for Mobile Traffic Grid Forecasting](https://mlsanity.com/paper/2603.12353/) (2026)
- [Spatiotemporal Graph Transformer for Traffic Intelligence in Edge Computing](https://mlsanity.com/paper/2608.04075/) (2026)
- [Trustworthy Data- and ML-Ops for Intelligent Transportation Systems and Logistics](https://mlsanity.com/paper/2610.01282/) (2026)

## Abstract (arXiv)

Forecasting with high accuracy the volume of data traffic that mobile users will consume is becoming increasingly important for precision traffic engineering, demand-aware network resource allocation, as well as public transportation. Measurements collection in dense urban deployments is however complex and expensive, and the post-processing required to make predictions is highly non-trivial, given the intricate spatio-temporal variability of mobile traffic due to user mobility. To overcome these challenges, in this paper we harness the exceptional feature extraction abilities of deep learning and propose a Spatio-Temporal neural Network (STN) architecture purposely designed for precise network-wide mobile traffic forecasting. We present a mechanism that fine tunes the STN and enables its operation with only limited ground truth observations. We then introduce a Double STN technique (D-STN), which uniquely combines the STN predictions with historical statistics, thereby making faithful long-term mobile traffic projections. Experiments we conduct with real-world mobile traffic data sets, collected over 60 days in both urban and rural areas, demonstrate that the proposed (D-)STN schemes perform up to 10-hour long predictions with remarkable accuracy, irrespective of the time of day when they are triggered. Specifically, our solutions achieve up to 61% smaller prediction errors as compared to widely used forecasting approaches, while operating with up to 600 times shorter measurement intervals.