streamad

Online anomaly detection for data streams/ Real-time anomaly detection for time series data.

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
Found CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
Found 2 DOI reference(s) in README
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Academic publication links
Links to: arxiv.org, springer.com, ieee.org, acm.org
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Committers with academic emails
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (15.5%) to scientific vocabulary

Keywords

anomaly-detection data-stream-processing data-streams incremental-learning machine-learning online-learning outlier-detection python real-time-detection streaming time-series timeseries

Last synced: 6 months ago · JSON representation ·

Repository

Online anomaly detection for data streams/ Real-time anomaly detection for time series data.

Basic Info

Host: GitHub
Owner: Fengrui-Liu
License: apache-2.0
Language: Python
Default Branch: main
Homepage: https://streamad.readthedocs.io/en/latest/
Size: 31 MB

Statistics

Stars: 123
Watchers: 6
Forks: 24
Open Issues: 3
Releases: 3

Topics

anomaly-detection data-stream-processing data-streams incremental-learning machine-learning online-learning outlier-detection python real-time-detection streaming time-series timeseries

Created over 5 years ago · Last pushed almost 3 years ago

Metadata Files

Readme License Citation

README.md

StreamAD

StreamAD Logo

Anomaly detection for data streams/time series. Detectors process the univariate or multivariate data one by one to simulte a real-time scene.

Documentation

PyPI PyPI - Python Version PyPI - Implementation

Read the Docs GitHub

Installation

The stable version can be installed from PyPI:

bash pip install streamad

The development version can be installed from GitHub:

bash pip install git+https://github.com/Fengrui-Liu/StreamAD

Quick Start

Start once detection within 5 lines of code. You can find more example with visualization results here.

```python from streamad.util import StreamGenerator, UnivariateDS from streamad.model import SpotDetector

ds = UnivariateDS() stream = StreamGenerator(ds.data) model = SpotDetector()

for x in stream.iteritem(): score = model.fitscore(x) ```

Models

For univariate time series

If you want to detect multivarite time series with these models, you need to apply them on each feature separately.

| Model Example | API Usage | Paper | | ----------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | KNNCAD | streamad.model.KNNDetector | Conformalized density- and distance-based anomaly detection in time-series data | | SPOT | streamad.model.SpotDetector | Anomaly detection in streams with extreme value theory | | Spectral Residual | streamad.model.SRDetector | Time-series anomaly detection service at microsoft | | Z score | streamad.model.ZScoreDetector | Standard score | | One-class SVM | streamad.model.OCSVMDetector | One-class SVM | | MAD | streamad.model.MadDetector | Median absolute deviation | | SARIMAX | streamad.model.SArimaDetector | Seasonal Arima Detector |

For multivariate time series

These models are compatible with univariate time series.

| Models Example | API Usage | Paper | | ------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | xStream | streamad.model.xStramDetector | Xstream: outlier detection in feature-evolving data streams | | RShash | streamad.model.RShashDetector | Subspace Outlier Detection in Linear Time with Randomized Hashing | | HSTree | streamad.model.HSTreeDetector | Fast Anomaly Detection for Streaming Data | | LODA | streamad.model.LodaDetector | Lightweight on-line detector of anomalies | | RRCF | streamad.model.RrcfDetector | Robust random cut forest based anomaly detection on streams |

Owner

Name: Fengrui Liu
Login: Fengrui-Liu
Kind: user
Location: Beijing, China
Company: Institute of Computing Technology, Chinese Academy of Sciences

Website: www.liufr.com
Repositories: 3
Profile: https://github.com/Fengrui-Liu

Love What You Do ❤️ Do What You Love

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Liu
    given-names: Fengrui
    orcid:
title: "StreamAD"
version: 0.3.0
doi:
date-released: 2022-05-15

GitHub Events

Total

Watch event: 13
Issue comment event: 1
Fork event: 1

Last Year

Watch event: 13
Issue comment event: 1
Fork event: 1

Committers

Last synced: almost 3 years ago

All Time

Total Commits: 227
Total Committers: 6
Avg Commits per committer: 37.833
Development Distribution Score (DDS): 0.062

Past Year

Commits: 151
Committers: 5
Avg Commits per committer: 30.2
Development Distribution Score (DDS): 0.04

Top Committers

Name	Email	Commits
Fengrui-Liu	l**z@i**n	213
lcx	1**2@q**m	7
Xujiahui21	9**1@u**m	3
lcx	3**5@u**m	2
Brandon Fergerson	b**n@a**g	1
Manuel Kaufmann	h**s@g**m	1

Committer Domains (Top 20 + Academic)

apache.org: 1 qq.com: 1 ict.ac.cn: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time

Total issues: 2
Total pull requests: 14
Average time to close issues: about 23 hours
Average time to close pull requests: 7 days
Total issue authors: 2
Total pull request authors: 7
Average comments per issue: 4.0
Average comments per pull request: 0.86
Merged pull requests: 8
Bot issues: 0
Bot pull requests: 2

Past Year

Issues: 1
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 1
Pull request authors: 0
Average comments per issue: 2.0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

stefanDeveloper (1)
BFergerson (1)

Pull Request Authors

Trailer5 (5)
Xujiahui21 (4)
fossabot (1)
dependabot[bot] (1)
humitos (1)
BFergerson (1)
lgtm-com[bot] (1)

Top Labels

Issue Labels

bug (2) enhancement (2) good first issue (1)

Pull Request Labels

function (3) new detector (3) documentation (1) dependencies (1)

Packages

Total packages: 1
Total downloads: unknown

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 1

conda-forge.org: streamad

Homepage: https://github.com/Fengrui-Liu/StreamAD
License: Apache-2.0
Latest release: 0.1.1
published almost 4 years ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent repos count: 34.0%

Stargazers count: 36.2%

Forks count: 37.3%

Average: 39.7%

Dependent packages count: 51.2%

Last synced: 6 months ago

Dependencies

benchmark/requirements_benchmark.txt pypi

kaleido *

docs/requirements-docs.txt pypi

jupytext *
myst_nb *
plotly *
recommonmark *
rrcf *
setuptools ==58.2.0
sphinx-book-theme *
sphinx-togglebutton *
sphinx_autodoc_typehints *
sphinx_copybutton *
sphinx_design *
sphinxcontrib.apidoc *
sphinxcontrib.bibtex *
tdigest *

.github/workflows/python-publish.yml actions

actions/checkout v3 composite
actions/setup-python v3 composite
pypa/gh-action-pypi-publish 27b31702a0e7fc50959f5ad993c78deac1bdfc29 composite

.github/workflows/testing.yml actions

actions/checkout master composite
actions/checkout v3 composite
actions/setup-python master composite
actions/setup-python v3 composite
codecov/codecov-action v2 composite
pypa/gh-action-pypi-publish v1.5.0 composite

poetry.lock pypi

accumulation-tree 0.6.2
colorama 0.4.6
coverage 7.2.5
exceptiongroup 1.1.1
fast-histogram 0.11
iniconfig 2.0.0
joblib 1.2.0
mmh3 3.1.0
numpy 1.24.3
packaging 23.1
pandas 1.5.3
patsy 0.5.3
plotly 5.14.1
pluggy 1.0.0
pytest 7.3.1
pytest-cov 4.0.0
python-dateutil 2.8.2
pytz 2023.3
pyudorandom 1.0.0
rrcf 0.4.4
scikit-learn 1.2.2
scipy 1.6.1
scipy 1.9.3
six 1.16.0
statsmodels 0.13.5
tdigest 0.5.2.2
tenacity 8.2.2
threadpoolctl 3.1.0
tomli 2.0.1

pyproject.toml pypi

fast-histogram ^0.11
mmh3 ^3.0.0
numpy ^1.22
pandas ^1.3.0
plotly ^5.14.1
pytest ^7.3.1
pytest-cov ^4.0.0
python ^3.8
rrcf ^0.4.4
scikit-learn ^1.0.0
scipy ^1.3.1
statsmodels 0.13.5
tdigest ^0.5.2.2