# Cellular Predictions on the Move: What about Data?

arXiv: https://arxiv.org/abs/2606.25709 · HTML: https://mlsanity.com/paper/2606.25709/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 47 references that are in the map.
- First posted on arXiv 24 Jun 2026.

## Builds on (most cited first, 10 of 10)

- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment](https://mlsanity.com/paper/1510.04026/) (2015)
- [A survey of results on mobile phone datasets analysis](https://mlsanity.com/paper/1502.03406/) (2015)
- [π-ROAD: a Learn-as-You-Go Framework for On-Demand Emergency Slices in V2X Scenarios](https://mlsanity.com/paper/2012.06208/) (2020)
- [UoMo: A Universal Model of Mobile Traffic Forecasting for Wireless Network Optimization](https://mlsanity.com/paper/2410.15322/) (2024)
- [On the road to more accurate mobile cellular traffic predictions](https://mlsanity.com/paper/2305.15234/) (2023)
- [Multi-grained spatial-temporal feature complementarity for accurate online cellular traffic prediction](https://mlsanity.com/paper/2508.08281/) (2025)
- [Data Matters: The Case of Predicting Mobile Cellular Traffic](https://mlsanity.com/paper/2411.02418/) (2024)
- [Mobile Application Traffic Reveals Multifunctional Use Patterns in Parisian Parks](https://mlsanity.com/paper/2508.15516/) (2025)
- [Sim-MSTNet: sim2real based Multi-task SpatioTemporal Network Traffic Forecasting](https://mlsanity.com/paper/2601.21384/) (2026)

## Abstract (arXiv)

Mobile cellular load forecasting is native to network resource optimization and delivery of services with reliability, latency and quality guarantees. The mainstream of machine learning research in the area is focused primarily on developing powerful learning structures for improved prediction accuracy. The data used for forecasting traditionally belong to the cellular domain and at most contain exogenous information about the surroundings of the base stations. We approach the prediction task from the perspective of data as a vital component of any data learning process. We hypothesize that substantial improvements could be achieved when the data inform on the processes that create the cellular load. Specifically, we propose to characterize the population dynamics -- the potential number of cellular traffic sources and their mobility -- in addition to employing historical time series of mobile data traffic. We validate our hypothesis for the rarely examined highway scenario. Comprehensive experiments show forecasting improvements on the order of 60% due to the use of these data alone.