📡 TelecomTS: A Multi-Modal Telecom Dataset

TelecomTS is a large-scale, high-resolution, multi-modal dataset derived from a 5G telecommunications testbed. It is the first public observability dataset to preserve deanonymized observability metrics with absolute scale information, encompassing by design various downstream tasks beyond forecasting such as anomaly detection, root-cause analysis, and multi-modal reasoning.

Observability data, particularly in telecommunications, differs fundamentally from conventional time series (e.g., weather, finance) by being:

• Zero-inflated
• Highly stochastic and bursty
• Structurally noisy with minimal discernible temporal patterns

🚀 Key Features

• 32k Data Samples
• 1M+ Observations from a live 5G network
• Multi-modal inputs:
  • Time series KPIs across PHY, MAC, and network layers
  • Environment descriptions and natural-language Q&A pairs
• Absolute scale preserved (no normalization/anonymization)
• Real and synthetic anomalies: 10 synthetic types grounded in telecom literature plus one real anomaly (jamming) collected over the air
• Reasoning traces: explicit chain-of-thought traces attached to network and anomalies Q&A entries, for reasoning-aware fine-tuning and RL
• Downstream tasks supported:
  • 🔎 Anomaly detection
  • 🛠️ Root-cause analysis
  • ⏱️ Anomaly duration localization
  • 📈 Forecasting / reconstruction
  • 🤖 Time series and network-level Q&A
• Labels provided: zone, application, mobility, congestion state, anomaly presence

📊 Statistics

📂 Dataset Structure

The main dataset consists of JSONL files containing chunked time series (128 timesteps each) along with multi-modal information. Each sample within the JSONL files includes:

• start_time / end_time — temporal boundaries of the chunk

• sampling_rate_hz — number of timesteps per second

• description — natural-language summary of the network environment and time series behaviors

• KPIs — key performance indicator names and values

• anomalies — existence, type, duration, affected KPIs, and troubleshooting tickets

• statistics — mean, variance, trend, and periodicity for each KPI

• labels — contextual metadata (zone, application, mobility, congestion, anomaly presence)

• QnA — natural-language Q&A over the sample, grouped into timeseries, network, and anomalies subcategories. Each entry of has the following structure:

  { "q": "What activity was the user engaged in?",
    "a": "Twitch",
    "reasoning": "Sustained downlink throughput in the 2–4 Mbps range with periodic UDP bursts and stable RSRP is consistent with live video streaming..." }
  

  The reasoning field, present in the last two subcategories, contains an explicit reasoning trace that reveals the intermediate decision-making steps used to derive the final answer.

Beyond the main dataset, each scenario also includes:

• description.txt – textual description of the network environment
• metrics.csv – raw observations for the scenario

🧪 Installation & Usage

Install 🤗 Datasets

pip install datasets

Load the Dataset

from datasets import load_dataset

# Load the full dataset
dataset = load_dataset(
          "AliMaatouk/TelecomTS",
          data_files={"full": "**/chunked.jsonl"}
)

print(dataset)

Inspect a Sample

sample = dataset["full"][0]
print(sample.keys())
# dict_keys(['start_time', 'end_time', 'sampling_rate', 'KPIs', 'description', 'anomalies', 'statistics', 'labels', 'QnA'])

Citation

You can find the paper with all details at https://arxiv.org/abs/2510.06063. Please cite it as follows:

@misc{feng2025telecomtsmultimodalobservabilitydataset,
      title={TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis}, 
      author={Austin Feng and Andreas Varvarigos and Ioannis Panitsas and Daniela Fernandez and Jinbiao Wei and Yuwei Guo and Jialin Chen and Ali Maatouk and Leandros Tassiulas and Rex Ying},
      year={2025},
      eprint={2510.06063},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2510.06063}, 
}