Duplicate from AliMaatouk/TelecomTS

Co-authored-by: Ali Maatouk <AliMaatouk@users.noreply.huggingface.co>

.gitattributes

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+*.pcm filter=lfs diff=lfs merge=lfs -text
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+# Audio files - compressed
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+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
+normal/stationary/Zone_A/no_congestion/File/processed/chunked.jsonl filter=lfs diff=lfs merge=lfs -text
+normal/stationary/Zone_A/no_congestion/File/raw/metrics.csv filter=lfs diff=lfs merge=lfs -text
+normal/stationary/Zone_A/no_congestion/Twitch/processed/chunked.jsonl filter=lfs diff=lfs merge=lfs -text
+normal/stationary/Zone_A/no_congestion/Twitch/raw/metrics.csv filter=lfs diff=lfs merge=lfs -text
+normal/stationary/Zone_A/no_congestion/YouTube/processed/chunked.jsonl filter=lfs diff=lfs merge=lfs -text
+normal/stationary/Zone_A/no_congestion/YouTube/raw/metrics.csv filter=lfs diff=lfs merge=lfs -text
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+normal/stationary/Zone_B/no_congestion/File/raw/metrics.csv filter=lfs diff=lfs merge=lfs -text
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+normal/stationary/Zone_B/no_congestion/Twitch/raw/metrics.csv filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: mit
+---
+# 📡 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**
+
+These characteristics make TelecomTS a challenging benchmark for both language and time series foundation models. Our benchmarking experiments reveal that state-of-the-art models struggle to capture the abrupt, noisy, and high-variance patterns inherent in observability streams.
+
+## 🚀 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 QnA pairs
+- **Absolute scale preserved** (no normalization/anonymization)
+- **Downstream tasks supported**:
+  - 📈 Forecasting
+  - 🔎 Anomaly detection
+  - 🛠️ Root-cause analysis
+  - 🤖 Multi-modal question answering (time series + text)
+- **Labels provided**: zone, application, mobility, congestion state, anomaly presence
+
+## 📂 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** - 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, anomaly duration, affected KPIs, and troubleshooting tickets
+- **Statistics** – mean, variance, and trends of the KPIs
+- **Labels** – contextual metadata (zone, application, mobility, congestion, presence of anomalies)
+- **QnA** – natural language reasoning tasks over the sample
+
+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
+```bash
+pip install datasets
+```
+
+### Load the Dataset
+```python
+from datasets import load_dataset
+
+# Load the full dataset
+dataset = load_dataset(
+          "AliMaatouk/TelecomTS",
+          data_files={"full": "**/chunked.jsonl"}
+)
+
+print(dataset)
+```
+
+### Inspect a Sample
+```python
+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:
+
+```bib
+@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}, 
+}
+```

anomalous/jammer/File/raw/description.txt

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+User is downloading a large file.
+User is static at zone 1.
+There is a jammer.
+The network is not congested.

anomalous/jammer/Twitch/raw/description.txt

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+User is watching a twitch stream.
+User is static at zone 1.
+There is a jammer.
+The network is not congested.

anomalous/jammer/YouTube/raw/description.txt

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+User is streaming youtube.
+User is static at zone 1.
+There is a jammer.
+The network is not congested.

anomalous/synthetic/Zone_A/File/raw/description.txt

@@ -0,0 +1,4 @@
+User is downloading a large file.
+User is static at zone 1.
+There are no jammers.
+The network is not congested.

anomalous/synthetic/Zone_A/Twitch/raw/description.txt

@@ -0,0 +1,4 @@
+User is watching a twitch stream.
+User is static at zone 1.
+There are no jammers.
+The network is not congested.

anomalous/synthetic/Zone_A/YouTube/raw/description.txt

@@ -0,0 +1,4 @@
+User is streaming youtube.
+User is static at zone 1.
+There are no jammers.
+The network is not congested.

anomalous/synthetic/Zone_B/File/raw/description.txt

@@ -0,0 +1,4 @@
+User is downloading a large file.
+User is static at zone 2.
+There are no jammers.
+The network is not congested.

anomalous/synthetic/Zone_B/Twitch/raw/description.txt

@@ -0,0 +1,4 @@
+User is watching a twitch stream.
+User is static at zone 2.
+There are no jammers.
+The network is not congested.

anomalous/synthetic/Zone_B/YouTube/raw/description.txt

@@ -0,0 +1,4 @@
+User is streaming youtube.
+User is static at zone 2.
+There are no jammers.
+The network is not congested.

anomalous/synthetic/Zone_C/File/raw/description.txt

@@ -0,0 +1,4 @@
+User is downloading a large file.
+User is static at zone 3.
+There are no jammers.
+The network is not congested.

anomalous/synthetic/Zone_C/Twitch/raw/description.txt

@@ -0,0 +1,4 @@
+User is watching a twitch stream.
+User is static at zone 3.
+There are no jammers.
+The network is not congested.

anomalous/synthetic/Zone_C/YouTube/raw/description.txt

@@ -0,0 +1,4 @@
+User is streaming youtube.
+User is static at zone 3.
+There are no jammers.
+The network is not congested.

normal/mobile/File/raw/description.txt

@@ -0,0 +1,4 @@
+User is downloading a large file.
+User is in motion.
+There are no jammers.
+The network is not congested.

normal/mobile/Twitch/raw/description.txt

@@ -0,0 +1,4 @@
+User is watching a twitch stream.
+User is in motion.
+There are no jammers.
+The network is not congested.

normal/mobile/YouTube/raw/description.txt

@@ -0,0 +1,4 @@
+User is streaming youtube.
+User is in motion.
+There are no jammers.
+The network is not congested.