Datasets:

techynilesh
/

streamarena

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

A Living Benchmark for Machine Learning on Streaming Data

🏆 Live leaderboard: techynilesh.github.io/StreamArena · Dataset catalog

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 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 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 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	23 files (10 real + 13 synthetic)	`real/`: electricity, forest cover, airlines, poker, weather, insects, Nomao, adult, power supply, sensor stream. `synth/`: classic drift generators (SEA, RBF, Hyperplane, Agrawal, random tree, blobs)
Regression	15 files (10 real + 5 synthetic)	`real/`: temporal/sensor streams (metro traffic, bike sharing, sarcos, elevators, ailerons, superconductivity, wave energy, video transcoding, california housing, fifa). `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	1 file	Credit-card fraud (time-ordered transaction stream). The former ODDS/ADBench static collection was removed in the 2026-08 audit — block-appended anomalies and i.i.d. tabular data don't test streaming detection

The catalog was pruned from 136 to 52 datasets in August 2026 after a leakage/stream-validity audit: removed datasets had target leakage in features, were sorted by class or target (the stream order gave away the answer), were duplicates of other entries, or were static i.i.d. tabular sets with no temporal structure.

Usage

pip install huggingface_hub

from huggingface_hub import snapshot_download

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

Or download just one task:

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:

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.

# 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)

# 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 and 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:

@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.