Datasets:

techynilesh
/

streamarena

	---
	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.