{
 "id": "netop/gotsf-ds",
 "owner": "netop",
 "name": "gotsf-ds",
 "downloads": 360,
 "likes": 13,
 "last_modified": "2025-12-24T15:57:54.000Z",
 "stated_licence": "mit",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [
  "time-series-forecasting"
 ],
 "citation": "This dataset is released alongside the following paper:\n\n> Fechete, L., et al. “Goal-Oriented Time-Series Forecasting: Foundation Framework Design.” Proceedings of the AAAI Conference on Artificial Intelligence, 2026, Singapore.\n\nIf you use this dataset, please cite the above work.\n\n\n---\n\n\nThis dataset introduces a **novel multivariate time series** specifically curated to support research in enabling **accurate prediction of KPIs** across communication networks, as illustrated below:\n\n<p align=\"center\">\n  <img src=\"images/network.png\" alt=\"Base station, cells, and beams\" />\n</p>\n\nPrecise 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.\n\n---",
 "signal": "permissive",
 "reason": "The declared licence (mit) is classified as permissive.",
 "revision": "c9ecaf153354b2cc415c64750a966c47c7f3f6ac",
 "stated_evidence": "https://huggingface.co/datasets/netop/gotsf-ds/blob/c9ecaf153354b2cc415c64750a966c47c7f3f6ac/README.md",
 "file_evidence": "https://huggingface.co/datasets/netop/gotsf-ds/tree/c9ecaf153354b2cc415c64750a966c47c7f3f6ac",
 "card_nc": false
}