---
license: mit
task_categories:
  - tabular-classification
  - tabular-regression
tags:
  - streaming
  - concept-drift
  - online-learning
  - anomaly-detection
  - clustering
  - tabular
pretty_name: StreamArena
---

<div align="center">
  <img src="https://huggingface.co/datasets/techynilesh/streamarena/resolve/main/assets/streamarena_logo.png" width="500" alt="StreamArena Logo"/>

  <p align="center"><strong>A Living Benchmark for Machine Learning on Streaming Data</strong></p>

  <br/>

  ![Python](https://img.shields.io/badge/-Python-3776AB?style=flat-square&logo=python&logoColor=white)
  ![Hugging Face](https://img.shields.io/badge/-Hugging%20Face-FFD21E?style=flat-square&logo=huggingface&logoColor=black)
  ![Streaming ML](https://img.shields.io/badge/-Streaming%20ML-0EA5E9?style=flat-square)
  ![Anomaly Detection](https://img.shields.io/badge/-Anomaly%20Detection-EF4444?style=flat-square)
  ![Classification](https://img.shields.io/badge/-Classification-16A34A?style=flat-square)
  ![Regression](https://img.shields.io/badge/-Regression-F59E0B?style=flat-square)
  ![Clustering](https://img.shields.io/badge/-Clustering-EC4899?style=flat-square)
  ![License MIT](https://img.shields.io/badge/-MIT%20License-000000?style=flat-square)
</div>

---

**🏆 Live leaderboard: [techynilesh.github.io/StreamArena](https://techynilesh.github.io/StreamArena/)** · [Dataset catalog](https://techynilesh.github.io/StreamArena/datasets.html)

StreamArena aggregates datasets for **stream learning** — classification, regression, clustering,
and anomaly detection under concept drift — into one consistently organized, task-first collection.
It plays the same role for streaming/online ML that [TabArena](https://github.com/autogluon/tabarena)
plays for tabular ML: a single place to find curated, ready-to-use datasets instead of hunting through
individual paper repos.

See the [GitHub repo](https://github.com/techynilesh/StreamArena) for loaders, examples, and a
`download.py` helper. Datasets were consolidated from several independent research codebases,
deduplicated where the same dataset appeared in multiple sources, and reorganized by **task**.
Every dataset is stored as a single unified format — **CSV** — chosen because it's what the
streaming-ML ecosystem (River's `stream.iter_csv`, MOA, scikit-multiflow's `FileStream`) actually
consumes row-by-row, unlike batch/columnar formats.

## Dataset structure

```
classification/
├── real/               # real-world streams (electricity, forest cover, airlines, ...)
└── synth/               # synthetic drift generators (SEA, RBF, Hyperplane, Agrawal, Madelon, ...)
regression/
├── real/               # housing, wages, sensor/physical measurements, ...
└── synth/               # Friedman & Hyperplane synthetic generators
clustering/
├── real/               # real-world streams reused from classification
└── synth/               # synthetic drift streams + blobs
anomaly_detection/        # ODDS/ADBench-style outlier detection sets (all real-world)
```

See [`DATASETS.md`](https://huggingface.co/datasets/techynilesh/streamarena/blob/main/DATASETS.md) for
the full per-dataset table — exact instance/feature/class counts computed directly from each file,
plus a best-effort source attribution (UCI, OpenML, DELVE, MOA/River generators, ODDS/ADBench, etc.)
for every dataset.

All files are `.csv`. Anomaly-detection files hold feature columns plus a trailing `label` column;
everything else follows the same feature-columns-plus-target convention. Every task except anomaly
detection (which is entirely real-world benchmark data) is split into `real/` and `synth/`.

| Task | Count | Notes |
|---|---:|---|
| **Classification** | 42 files (22 real + 20 synthetic) | `real/`: electricity, forest cover, airlines, poker, weather, KDD-99, insects, Nomao, MNIST, Usenet, Gisette, Dota, Spambase, HAR, etc. `synth/`: classic drift generators (SEA, RBF, Hyperplane, Agrawal, Madelon) |
| **Regression** | 30 files (25 real + 5 synthetic) | `real/`: housing (king's county, california, miami, brazilian), wages, sensor/physical (sarcos, naval propulsion, superconductivity, kin8nm), and more. `synth/`: Friedman & Hyperplane generators |
| **Clustering** | 13 files (6 real + 7 synthetic) | Streaming clustering benchmarks — reuses classification drift streams plus a dedicated synthetic blobs set |
| **Anomaly Detection** | 51 files | ODDS/ADBench-style outlier detection collection (annthyroid, mnist, shuttle, satellite, mammography, etc.) — all real-world, no `real/`/`synth/` split |

## Usage

```bash
pip install huggingface_hub
```

```python
from huggingface_hub import snapshot_download

path = snapshot_download(repo_id="techynilesh/streamarena", repo_type="dataset")
```

Or download just one task:

```python
from huggingface_hub import snapshot_download

path = snapshot_download(
    repo_id="techynilesh/streamarena",
    repo_type="dataset",
    allow_patterns=["classification/**"],
)
```

Then load files directly — it's always just a CSV:

```python
import pandas as pd
df = pd.read_csv(f"{path}/classification/real/electricity.csv")
```

### Using it with River or CapyMOA

Since every dataset is plain CSV, it plugs directly into the two most common Python streaming-ML
libraries — no conversion needed.

```python
# River
import pandas as pd
from river import metrics, stream, tree

path = "classification/real/electricity.csv"
sample = pd.read_csv(path, nrows=100)
target = sample.columns[-1]
# Convert only numeric feature columns to float; categorical/string columns
# (e.g. in adult.csv) pass through as-is — River trees handle them natively.
converters = {
    c: float for c in sample.columns[:-1] if pd.api.types.is_numeric_dtype(sample[c])
}

dataset = stream.iter_csv(path, target=target, converters=converters)
model = tree.HoeffdingTreeClassifier()
metric = metrics.Accuracy()

for x, y in dataset:
    y_pred = model.predict_one(x)
    model.learn_one(x, y)
    metric.update(y, y_pred)

print(metric)
```

```python
# CapyMOA (requires a working JVM — Java 11+)
from capymoa.classifier import HoeffdingTree
from capymoa.evaluation import prequential_evaluation
from capymoa.stream import stream_from_file

stream = stream_from_file(
    "classification/real/electricity.csv",
    dataset_name="Electricity",
    class_index=-1,       # StreamArena's convention: label is the trailing column
    target_type="categorical",
)
learner = HoeffdingTree(schema=stream.get_schema())
results = prequential_evaluation(stream, learner)
print("accuracy:", results.cumulative.accuracy())
```

See [`examples/river_usage.py`](https://github.com/TechyNilesh/StreamArena/blob/main/examples/river_usage.py)
and [`examples/capymoa_usage.py`](https://github.com/TechyNilesh/StreamArena/blob/main/examples/capymoa_usage.py)
on GitHub for the full runnable scripts.

## License

MIT for the aggregation/curation. Individual datasets retain their original licenses/terms from
their respective sources — check before redistribution.

## Citation

If you use StreamArena in your research, please cite it as below:

```bibtex
@misc{verma2026streamarena,
  title  = {StreamArena: A Living Benchmark for Machine Learning on Streaming Data},
  author = {Verma, Nilesh},
  year   = {2026},
  url    = {https://github.com/TechyNilesh/StreamArena}
}
```

Please also cite the original dataset sources where applicable.