netop/Beam-Level-Traffic-Timeseries-Dataset

Beam-Level (5G) Time-Series Dataset

This dataset presents a novel, multi-variate time series specifically designed for advancing research in spatio-temporal forecasting. Our primary goal is to facilitate the accurate prediction of traffic throughput volumes across communication networks, as visually depicted in Figure 1.

The precise forecasting of network traffic volume is crucial for optimizing network flow management and efficient resource allocation. Consequently, this task holds significant practical and theoretical relevance for the scientific community in both networking and machine learning domains. The dataset aims to provide a valuable benchmark for researchers exploring state-of-the-art (SOTA) techniques in time series analysis.

Dataset Split

This repository contains four datasets containing network performance metrics for 2,880 beams across 30 base stations. Each base station consists of 3 cells with 32 beams, with data recorded hourly. These datasets encompass a five-week period with data recorded at hourly intervals (as illustrated in Figure 2). These datasets are traffic_DLThpVol.csv, traffic_DLThpTime.csv, traffic_MR_number.csv, and traffic_DLPRB.csv. We remind the participants that the objective is to forecast future values of traffic volume (DLThpVol).

Each dataset corresponds to a specific network performance metric:

• traffic_DLThpVol.csv: represents throughput volume.
• traffic_DLThpTime.csv: represents throughput time.
• traffic_ DLPRB.csv: represents Physical Resource Block (PRB) utilization.
• traffic_MR_number.csv: represents user count.

Citation

Please cite this paper if you intend to use this dataset for your research:

L. Fechete et al., Goal-Oriented Time-Series Forecasting: Foundation Framework Design, arXiv:2504.17493 (2025).

The code associated with the dataset is provided here.