metadata

viewer: false
license: mit
task_categories:
  - time-series-forecasting
task_ids:
  - univariate-time-series-forecasting
  - multivariate-time-series-forecasting
pretty_name: Beam-Level (5G) Time-Series Dataset
configs:
  - config_name: DLPRB
    description: Downlink Physical Resource Block (DLPRB) time-series data.
    data_files:
      - split: train_0w_5w
        path: data/train/DLPRB_train_0w-5w.csv
      - split: test_5w_6w
        path: data/test/DLPRB_test_5w-6w.csv
      - split: test_10w_11w
        path: data/test/DLPRB_test_10w-11w.csv
  - config_name: DLThpVol
    description: Downlink Throughput Volume (DLThpVol) time-series data.
    data_files:
      - split: train_0w_5w
        path: data/train/DLThpVol_train_0w-5w.csv
      - split: test_5w_6w
        path: data/test/DLThpVol_test_5w-6w.csv
      - split: test_10w_11w
        path: data/test/DLThpVol_test_10w-11w.csv
  - config_name: DLThpTime
    description: Downlink Throughput Time (DLThpTime) time-series data.
    data_files:
      - split: train_0w_5w
        path: data/train/DLThpTime_train_0w-5w.csv
      - split: test_5w_6w
        path: data/test/DLThpTime_test_5w-6w.csv
      - split: test_10w_11w
        path: data/test/DLThpTime_test_10w-11w.csv
  - config_name: MR_number
    description: Measurement Report Number (MR_number) time-series data.
    data_files:
      - split: train_0w_5w
        path: data/train/MR_number_train_0w-5w.csv
      - split: test_5w_6w
        path: data/test/MR_number_test_5w-6w.csv
      - split: test_10w_11w
        path: data/test/MR_number_test_10w-11w.csv
language:
  - en
tags:
  - wireless

📶 Beam-Level (5G) Time-Series Dataset

This dataset introduces a novel multivariate time series specifically curated to support research in enabling accurate prediction of KPIs across communication networks, as illustrated below:

Base station, cells, and beams

Precise forecasting of network traffic is critical for optimizing network management and enhancing resource allocation efficiency. This task is of both practical and theoretical importance to researchers in networking and machine learning, offering a strong benchmark for state-of-the-art (SOTA) time series models.

📂 Dataset Overview

The dataset comprises:

2,880 Beams across 30 Base Stations (3 Cells per Station, 32 Beams per Cell).
Duration: 5 weeks + 2 target weeks, totaling up to 840 training hours and 1176 total hours per beam.

📁 Available CSV Files

🏋️‍♂️ Training Set (Weeks 0–5)

File Name	Metric
`DLThpVol_train_0w-5w.csv`	Downlink throughput volume
`DLThpTime_train_0w-5w.csv`	Throughput transmission time
`DLPRB_train_0w-5w.csv`	PRB (Physical Resource Block) usage
`MR_number_train_0w-5w.csv`	User count (Measurement Reports)

🎯 Forecast Targets

📆 6th Week (Week 5–6)

File Name	Metric
`DLThpVol_test_5w-6w.csv`	Downlink throughput volume
`DLThpTime_test_5w-6w.csv`	Throughput transmission time
`DLPRB_test_5w-6w.csv`	PRB usage
`MR_number_test_5w-6w.csv`	User count

📆 11th Week (Week 10–11)

File Name	Metric
`DLThpVol_test_10w-11w.csv`	Downlink throughput volume
`DLThpTime_test_10w-11w.csv`	Throughput transmission time
`DLPRB_test_10w-11w.csv`	PRB usage
`MR_number_test_10w-11w.csv`	User count

🧪 Dataset Splits

Dataset train/forecast split

The dataset is split into a Training Set (first 5 weeks) and Forecast Targets for Week 6 (immediate future) and Week 11 (long-term future).

📄 Data Format

Each CSV file contains a Time column and multiple beam columns (e.g., 0_0_0 to 29_2_31). The Time column ranges from 0–839 for training (weeks 1–6), 0–167 for week 6, and 168–335 for week 11. Each beam column uniquely identifies one of the 2,880 beams across 30 base stations.

📚 Citation

If you use this dataset in your research, please cite:

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

🔗 Code Repository

The official codebase for working with this dataset is available here: 👉 https://github.com/netop-team/gotsf