| Name | pyoats JSON |
| Version |
0.1.4
JSON |
| download |
| home_page | None |
| Summary | Quick and Easy Time Series Outlier Detection |
| upload_time | 2024-05-06 22:14:34 |
| maintainer | None |
| docs_url | None |
| author | Benjamin Ye |
| requires_python | <3.12,>=3.9 |
| license | Apache-2.0 |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<!-- Improved compatibility of back to top link: See: https://github.com/othneildrew/Best-README-Template/pull/73 -->
<a name="readme-top"></a>
<!--
*** Thanks for checking out the Best-README-Template. If you have a suggestion
*** that would make this better, please fork the repo and create a pull request
*** or simply open an issue with the tag "enhancement".
*** Don't forget to give the project a star!
*** Thanks again! Now go create something AMAZING! :D
-->
<!-- PROJECT SHIELDS -->
<!--
*** I'm using markdown "reference style" links for readability.
*** Reference links are enclosed in brackets [ ] instead of parentheses ( ).
*** See the bottom of this document for the declaration of the reference variables
*** for contributors-url, forks-url, etc. This is an optional, concise syntax you may use.
*** https://www.markdownguide.org/basic-syntax/#reference-style-links
-->
[![Contributors][contributors-shield]][contributors-url]
[![Stargazers][stars-shield]][stars-url]
[![Issues][issues-shield]][issues-url]
[![Apache 2.0 License][license-shield]][license-url]
[![Last Commit][last_commit-shield]][last_commit-url]
<!-- PROJECT LOGO -->
<br />
<div align="center">
<a href="https://github.com/georgian-io/oats">
<img src="https://github.com/georgian-io/pyoats/raw/main/static/oats.png" alt="Logo" width="auto" height="80">
</a>
<h3 align="center"> OATS</h3>
<p align="center">
Quick and Easy Outlier Detection for Time Series
<br />
<a href="https://georgian-io-archive.github.io/pyoats-docs/"><strong>Explore the docs »</strong></a>
<br />
<br />
<a href="https://georgian-io.medium.com/time-series-anomaly-detection-a-field-study-d13b35ee4344">View Demo</a>
·
<a href="https://github.com/georgian-io/pyoats/issues">Report Bug</a>
·
<a href="https://github.com/georgian-io/pyoats/issues">Request Feature</a>
</p>
</div>
<!-- TABLE OF CONTENTS -->
<details>
<summary>Table of Contents</summary>
<ol>
<li>
<a href="#about-the-project">About The Project</a>
<ul>
<li><a href="#built-with">Built With</a></li>
</ul>
</li>
<li>
<a href="#getting-started">Getting Started</a>
<ul>
<li><a href="#prerequisites">Prerequisites</a></li>
<li><a href="#installation">Installation</a></li>
</ul>
</li>
<li><a href="#usage">Usage</a></li>
<li><a href="#models">Models</a></li>
<li><a href="#roadmap">Roadmap</a></li>
<li><a href="#contributing">Contributing</a></li>
<li><a href="#license">License</a></li>
<li><a href="#contact">Contact</a></li>
<li><a href="#acknowledgments">Acknowledgments</a></li>
</ol>
</details>
<!-- ABOUT THE PROJECT -->
## About The Project
Adapting existing outlier detection & prediction methods into a **time series outlier detection** system is not a simple task. Good news: **OATS** has done the heavy lifting for you!
We present a straight-forward interface for popular, state-of-the-art detection methods to assist you in your experiments. In addition to the models, we also present different options when it comes to selecting a final threshold for predictions.
**OATS** seamlessly supports both univariate and multivariate time series regardless of the model choice and guarantees the same output shape, enabling a modular approach to time series anoamly detection.
<p align="right">(<a href="#readme-top">back to top</a>)</p>
### Built With
[![Python][Python.org]][Python-url] [![Poetry][Python-Poetry.org]][Poetry-url]
[![Pytorch][Pytorch.org]][Torch-url] [![PytorchLightning][PytorchLightning.ai]][Lightning-url] [![TensorFlow][TensorFlow.org]][TF-url] [![Numpy][Numpy.org]][Numpy-url]
[![Darts][Darts]][Darts-url] [![PyOD][PyOD]][PyOD-url]
<p align="right">(<a href="#readme-top">back to top</a>)</p>
<!-- GETTING STARTED -->
## Getting Started
<br />
<div align="center">
<img src="https://github.com/georgian-io/pyoats/raw/main/static/example-sine_wave.png" alt="Usage Example" width="600" height="auto">
</a>
</div>
### Prerequisites
[![Python][Python.org]][Python-url] >=3.8, <3.11
#### For Docker Install:
[![Docker][Docker.com]][Docker-url]
#### For Local Install:
[![Poetry][Python-Poetry.org]][Poetry-url]
### Installation
#### PyPI
1. Install package via pip
```sh
pip install pyoats
```
**❗ Installing using an environment manager such as [`conda`](https://docs.conda.io/en/latest/miniconda.html), [`venv`](https://docs.python.org/3/library/venv.html), and [`poetry`](https://python-poetry.org/) is highly encouraged as this package contains deep learning frameworks.**
#### Docker
1. Clone the repo
```sh
git clone https://github.com/georgian-io/pyoats.git && cd pyoats
```
2. Build image
```sh
docker build -t pyoats .
```
3. Run Container
```sh
# CPU Only
docker run -it pyoats
# with GPU
docker run -it --gpus all pyoats
```
#### Local
1. Clone the repo
```sh
git clone https://github.com/georgian-io/pyoats.git && cd pyoats
```
2. Install via Poetry
```sh
poetry install
```
<p align="right">(<a href="#readme-top">back to top</a>)</p>
<!-- USAGE EXAMPLES -->
## Usage
### Quick Start
For a quick start, please refer to <a href="https://medium.com/@georgian-io/time-series-anomaly-detection-a-field-study-d13b35ee4344">our blog</a> or copy our <a href="https://tinyurl.com/pyoats-notebook">Colab notebook</a>!
### Getting Anomaly Score
```python
from oats.models import NHiTSModel
model = NHiTSModel(window=20, use_gpu=True)
model.fit(train)
scores = model.get_scores(test)
```
### Getting Threshold
```python
from oats.threshold import QuantileThreshold
t = QuantileThreshold()
threshold = t.get_threshold(scores, 0.99)
anom = scores > threshold
```
_For more examples, please refer to the [Documentation](https://georgian-io.github.io/pyoats-docs/)_
<p align="right">(<a href="#readme-top">back to top</a>)</p>
<!-- Models -->
## Models
_For more details about the individual models, please refer to the [Documentation](https://georgian-io.github.io/pyoats-docs/) or <a href="https://medium.com/georgian-impact-blog/time-series-anomaly-detection-the-detectives-toolbox-9ef131dddaf9">this blog</a> for deeper explanation._
Model | Type | Multivariate Support* | Requires Fitting | DL Framework Dependency | Paper | Reference Model
--- | :---: | :---: | :---: | :---: | :---: | :---:
`ARIMA` | Predictive | ⚠️ | ✅ | | | [`statsmodels.ARIMA`](https://www.statsmodels.org/dev/generated/statsmodels.tsa.arima.model.ARIMA.html)
`FluxEV` | Predictive | ⚠️ | ✅ | | [📝](https://dl.acm.org/doi/10.1145/3437963.3441823) |
`LightGBM` | Predictive | ⚠️ | ✅ | | | [`darts.LightGBM`](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.gradient_boosted_model.html)
`Moving Average` | Predictive | ⚠️ | | | |
`N-BEATS` | Predictive | ✅ | ✅ | [![Pytorch][Pytorch.org]][Torch-url] | [📝](https://openreview.net/forum?id=r1ecqn4YwB) | [`darts.NBEATS`](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nbeats.html)
`N-HiTS` | Predictive | ✅ | ✅ | [![Pytorch][Pytorch.org]][Torch-url] | [📝](https://arxiv.org/abs/2201.12886) | [`darts.NHiTS`](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nhits.html)
`RandomForest` | Predictive | ⚠️ | ✅ | | | [`darts.RandomForest`](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.random_forest.html)
`Regression` | Predictive | ⚠️ | ✅ | | | [`darts.Regression`](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_model.html)
`RNN` | Predictive | ✅ | ✅ | [![Pytorch][Pytorch.org]][Torch-url] | | [`darts.RNN`](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.rnn_model.html)
`Temporal Convolution Network` | Predictive | ✅ | ✅ | [![Pytorch][Pytorch.org]][Torch-url] | [📝](https://arxiv.org/abs/1803.01271) | [`darts.TCN`](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tcn_model.html)
`Temporal Fusion Transformers` | Predictive | ✅ | ✅ | [![Pytorch][Pytorch.org]][Torch-url] | [📝](https://arxiv.org/abs/1912.09363) | [`darts.TFT`](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html)
`Transformer` | Predictive | ✅ | ✅ | [![Pytorch][Pytorch.org]][Torch-url] | [📝](https://arxiv.org/abs/1706.03762) | [`darts.Transformer`](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.transformer_model.html)
`Isolation Forest` | Distance-Based | ✅ | ✅ | || [`pyod.IForest`](https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.iforest)
`Matrix Profile` | Distance-Based | ✅ | | | [📝](https://www.cs.ucr.edu/~eamonn/MatrixProfile.html) | [`stumpy`](https://github.com/TDAmeritrade/stumpy)
`TranAD` | Reconstruction-Based | ✅ | ✅ | [![TensorFlow][TensorFlow.org]][Torch-url] | [📝](https://arxiv.org/abs/2201.07284) | [`tranad`](https://github.com/imperial-qore/TranAD)
`Variational Autoencoder` | Reconstruction-Based | ✅ | ✅ | [![TensorFlow][TensorFlow.org]][Torch-url] | [📝](https://arxiv.org/abs/1312.6114) | [`pyod.VAE`](https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.vae)
`Quantile` | Rule-Based | ⚠️ | | ||
**\*** For models with ⚠️, score calculation is done separately along each column. This implicitly assumes independence of covariates, which means that **the resultant anomaly scores do not take into account of inter-variable dependency structures.**
<p align="right">(<a href="#readme-top">back to top</a>)</p>
<!-- ROADMAP -->
## Roadmap
- [ ] Automatic hyper-parameter tuning
- [ ] More examples
- [ ] More preprocessors
- [ ] More models from `pyod`
See the [open issues](https://github.com/georgian-io/pyoats/issues) for a full list of proposed features (and known issues).
<p align="right">(<a href="#readme-top">back to top</a>)</p>
<!-- CONTRIBUTING -->
## Contributing
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!
1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/amazing_feature`)
3. Commit your Changes (`git commit -m 'Add some amazing_feature'`)
4. Push to the Branch (`git push origin feature/amazing_feature`)
5. Open a Pull Request
<p align="right">(<a href="#readme-top">back to top</a>)</p>
<!-- LICENSE -->
## License
Distributed under the Apache 2.0 License. See `LICENSE` for more information.
<p align="right">(<a href="#readme-top">back to top</a>)</p>
<!-- CONTACT -->
## Contact
<div align="left">
<a href="https://www.georgian.io">
<img src="https://s34372.pcdn.co/wp-content/uploads/2022/03/Georgian_Blue.png" alt="Logo" width="auto" height="80">
</a>
</div>
|<!-- -->|<!-- -->|<!-- -->|<!-- -->|
|---|---|---|---|
| __Benjamin Ye__ | [![Github][BenGithub]][BenLinkedIn-url] | [![LinkedIn][BenLinkedIn]][BenLinkedIn-url] | [![eMail][eMail]][BenEmail-url]
Project Link: [https://github.com/georgian-io/oats](https://github.com/georgian-io/oats)
<p align="right">(<a href="#readme-top">back to top</a>)</p>
<!-- ACKNOWLEDGMENTS -->
## Acknowledgments
I would like to thank my colleagues from Georgian for all the help and advice provided along the way.
* [Angeline Yasodhara](mailto:angeline@georgian.io)
* [Akshay Budhkar](mailto:akshay@georgian.io)
* [Borna Almasi](mailto:borna@georgian.io)
* [Parinaz Sobhani](mailto:parinaz@georgian.io)
* [Rodrigo Ceballos Lentini](mailto:rodrigo@georgian.io)
I'd also like to extend my gratitude to all the contributors at [`Darts`][Darts-url] (for time series predictions) and [`PyOD`][PyOD-url] (for general outlier detection), whose projects have enabled a straight-forward extension into the domain of time series anomaly detection.
Finally, it'll be remiss of me to not mention [DATA Lab @ Rice University](https://cs.rice.edu/~xh37/index.html), whose wonderful [`TODS`][TODS-url] package served as a major inspiration for this project. Please check them out especially if you're looking for AutoML support.
[![Darts][Darts]][Darts-url] [![PyOD][PyOD]][PyOD-url] [![TODS][TODS]][TODS-url]
<p align="right">(<a href="#readme-top">back to top</a>)</p>
<!-- MARKDOWN LINKS & IMAGES -->
<!-- https://www.markdownguide.org/basic-syntax/#reference-style-links -->
[contributors-shield]: https://img.shields.io/github/contributors/georgian-io/oats.svg?style=for-the-badge
[contributors-url]: https://github.com/georgian-io/pyoats/graphs/contributors
[forks-shield]: https://img.shields.io/github/forks/georgian-io/contributors.svg?style=for-the-badge
[forks-url]: https://github.com/georgian-io/pyoats/network/members
[stars-shield]: https://img.shields.io/github/stars/georgian-io/oats.svg?style=for-the-badge
[stars-url]: https://github.com/georgian-io/pyoats/stargazers
[issues-shield]: https://img.shields.io/github/issues/georgian-io/oats.svg?style=for-the-badge
[issues-url]: https://github.com/georgian-io/pyoats/issues
[license-shield]: https://img.shields.io/github/license/georgian-io/oats.svg?style=for-the-badge
[license-url]: https://github.com/georgian-io/pyoats/blob/master/LICENSE
[last_commit-shield]: https://img.shields.io/github/last-commit/georgian-io/oats.svg?style=for-the-badge
[last_commit-url]: https://github.com/georgian-io/pyoats/commits/
<!-- Deps Links -->
[Python-Poetry.org]: https://img.shields.io/badge/Poetry-60A5FA?style=for-the-badge&logo=poetry&logoColor=white
[Poetry-url]: https://www.python-poetry.org/
[Python.org]: https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=python&logoColor=white
[Python-url]: https://www.python.org/
[PyTorch.org]: https://img.shields.io/badge/PyTorch-EE4C2C?style=for-the-badge&logo=pytorch&logoColor=white
[Torch-url]: https://pytorch.org/
[TensorFlow.org]: https://img.shields.io/badge/TensorFlow-FF6F00?style=for-the-badge&logo=tensorflow&logoColor=white
[TF-url]: https://www.tensorflow.org/
[Numpy.org]: https://img.shields.io/badge/Numpy-013243?style=for-the-badge&logo=numpy&logoColor=white
[Numpy-url]: https://www.numpy.org/
[Darts]: https://img.shields.io/badge/Repo-Darts-2100FF?style=for-the-badge&logo=github&logoColor=white
[Darts-url]: https://github.com/unit8co/darts
[PyOD]: https://img.shields.io/badge/Repo-PyOD-000000?style=for-the-badge&logo=github&logoColor=white
[PyOD-url]: https://github.com/yzhao062/pyod
[TODS]: https://img.shields.io/badge/Repo-TODS-29B48C?style=for-the-badge&logo=github&logoColor=white
[TODS-url]: https://github.com/datamllab/tods
[Docker.com]: https://img.shields.io/badge/Docker-2496ED?style=for-the-badge&logo=docker&logoColor=white
[Docker-url]: https://docker.com
[PyTorchLightning.ai]: https://img.shields.io/badge/lightning-792EE5?style=for-the-badge&logo=pytorchlightning&logoColor=white
[Lightning-url]: https://www.pytorchlightning.ai/
[BenLinkedIn]: https://img.shields.io/badge/LinkedIn-0A66C2?style=for-the-badge&logo=linkedin&logoColor=white
[BenLinkedIn-url]: https://www.linkedin.com/in/benjaminye/
[eMail]: https://img.shields.io/badge/EMail-EA4335?style=for-the-badge&logo=gmail&logoColor=white
[BenEmail-url]: mailto:benjamin.ye@georgian.io
[BenGithub]: https://img.shields.io/badge/Profile-14334A?style=for-the-badge&logo=github&logoColor=white
[BenGithub-url]: https://www.linkedin.com/in/benjaminye/
Raw data
{
"_id": null,
"home_page": null,
"name": "pyoats",
"maintainer": null,
"docs_url": null,
"requires_python": "<3.12,>=3.9",
"maintainer_email": null,
"keywords": null,
"author": "Benjamin Ye",
"author_email": "benjamin.ye@georgian.io",
"download_url": "https://files.pythonhosted.org/packages/74/f8/aea6d2043d930c971aea23cbd22e0a22c9f827b0980e1a0f13e0d459bb1f/pyoats-0.1.4.tar.gz",
"platform": null,
"description": "<!-- Improved compatibility of back to top link: See: https://github.com/othneildrew/Best-README-Template/pull/73 -->\n<a name=\"readme-top\"></a>\n<!--\n*** Thanks for checking out the Best-README-Template. If you have a suggestion\n*** that would make this better, please fork the repo and create a pull request\n*** or simply open an issue with the tag \"enhancement\".\n*** Don't forget to give the project a star!\n*** Thanks again! Now go create something AMAZING! :D\n-->\n\n\n\n<!-- PROJECT SHIELDS -->\n<!--\n*** I'm using markdown \"reference style\" links for readability.\n*** Reference links are enclosed in brackets [ ] instead of parentheses ( ).\n*** See the bottom of this document for the declaration of the reference variables\n*** for contributors-url, forks-url, etc. This is an optional, concise syntax you may use.\n*** https://www.markdownguide.org/basic-syntax/#reference-style-links\n-->\n[![Contributors][contributors-shield]][contributors-url]\n[![Stargazers][stars-shield]][stars-url]\n[![Issues][issues-shield]][issues-url]\n[![Apache 2.0 License][license-shield]][license-url]\n[![Last Commit][last_commit-shield]][last_commit-url]\n\n\n\n<!-- PROJECT LOGO -->\n<br />\n<div align=\"center\">\n <a href=\"https://github.com/georgian-io/oats\">\n <img src=\"https://github.com/georgian-io/pyoats/raw/main/static/oats.png\" alt=\"Logo\" width=\"auto\" height=\"80\">\n </a>\n\n<h3 align=\"center\"> OATS</h3>\n\n <p align=\"center\">\n Quick and Easy Outlier Detection for Time Series \n <br />\n <a href=\"https://georgian-io-archive.github.io/pyoats-docs/\"><strong>Explore the docs \u00bb</strong></a>\n <br />\n <br />\n <a href=\"https://georgian-io.medium.com/time-series-anomaly-detection-a-field-study-d13b35ee4344\">View Demo</a>\n \u00b7\n <a href=\"https://github.com/georgian-io/pyoats/issues\">Report Bug</a>\n \u00b7\n <a href=\"https://github.com/georgian-io/pyoats/issues\">Request Feature</a>\n </p>\n</div>\n\n\n\n<!-- TABLE OF CONTENTS -->\n<details>\n <summary>Table of Contents</summary>\n <ol>\n <li>\n <a href=\"#about-the-project\">About The Project</a>\n <ul>\n <li><a href=\"#built-with\">Built With</a></li>\n </ul>\n </li>\n <li>\n <a href=\"#getting-started\">Getting Started</a>\n <ul>\n <li><a href=\"#prerequisites\">Prerequisites</a></li>\n <li><a href=\"#installation\">Installation</a></li>\n </ul>\n </li>\n <li><a href=\"#usage\">Usage</a></li>\n <li><a href=\"#models\">Models</a></li>\n <li><a href=\"#roadmap\">Roadmap</a></li>\n <li><a href=\"#contributing\">Contributing</a></li>\n <li><a href=\"#license\">License</a></li>\n <li><a href=\"#contact\">Contact</a></li>\n <li><a href=\"#acknowledgments\">Acknowledgments</a></li>\n </ol>\n</details>\n\n\n\n<!-- ABOUT THE PROJECT -->\n## About The Project\nAdapting existing outlier detection & prediction methods into a **time series outlier detection** system is not a simple task. Good news: **OATS** has done the heavy lifting for you! \n\nWe present a straight-forward interface for popular, state-of-the-art detection methods to assist you in your experiments. In addition to the models, we also present different options when it comes to selecting a final threshold for predictions.\n\n**OATS** seamlessly supports both univariate and multivariate time series regardless of the model choice and guarantees the same output shape, enabling a modular approach to time series anoamly detection.\n\n\n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\n\n\n\n### Built With\n[![Python][Python.org]][Python-url] [![Poetry][Python-Poetry.org]][Poetry-url]\n\n[![Pytorch][Pytorch.org]][Torch-url] [![PytorchLightning][PytorchLightning.ai]][Lightning-url] [![TensorFlow][TensorFlow.org]][TF-url] [![Numpy][Numpy.org]][Numpy-url]\n\n[![Darts][Darts]][Darts-url] [![PyOD][PyOD]][PyOD-url]\n\n\n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\n\n\n\n<!-- GETTING STARTED -->\n## Getting Started\n<br />\n<div align=\"center\">\n <img src=\"https://github.com/georgian-io/pyoats/raw/main/static/example-sine_wave.png\" alt=\"Usage Example\" width=\"600\" height=\"auto\">\n </a>\n </div>\n\n\n### Prerequisites\n[![Python][Python.org]][Python-url] >=3.8, <3.11\n\n\n#### For Docker Install:\n\n[![Docker][Docker.com]][Docker-url]\n\n#### For Local Install:\n\n[![Poetry][Python-Poetry.org]][Poetry-url]\n\n### Installation\n#### PyPI\n1. Install package via pip\n ```sh\n pip install pyoats\n ```\n **\u2757 Installing using an environment manager such as [`conda`](https://docs.conda.io/en/latest/miniconda.html), [`venv`](https://docs.python.org/3/library/venv.html), and [`poetry`](https://python-poetry.org/) is highly encouraged as this package contains deep learning frameworks.**\n \n#### Docker\n1. Clone the repo\n ```sh\n git clone https://github.com/georgian-io/pyoats.git && cd pyoats \n ```\n2. Build image\n ```sh\n docker build -t pyoats . \n ```\n3. Run Container\n ```sh \n # CPU Only\n docker run -it pyoats\n \n # with GPU\n docker run -it --gpus all pyoats\n ```\n \n#### Local\n1. Clone the repo\n ```sh\n git clone https://github.com/georgian-io/pyoats.git && cd pyoats \n ```\n2. Install via Poetry\n ```sh \n poetry install\n ```\n\n\n\n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\n\n\n\n<!-- USAGE EXAMPLES -->\n## Usage\n\n### Quick Start\nFor a quick start, please refer to <a href=\"https://medium.com/@georgian-io/time-series-anomaly-detection-a-field-study-d13b35ee4344\">our blog</a> or copy our <a href=\"https://tinyurl.com/pyoats-notebook\">Colab notebook</a>!\n\n\n### Getting Anomaly Score\n```python \nfrom oats.models import NHiTSModel\n\nmodel = NHiTSModel(window=20, use_gpu=True)\nmodel.fit(train)\nscores = model.get_scores(test)\n```\n### Getting Threshold\n```python \nfrom oats.threshold import QuantileThreshold\n\nt = QuantileThreshold()\nthreshold = t.get_threshold(scores, 0.99)\nanom = scores > threshold\n```\n_For more examples, please refer to the [Documentation](https://georgian-io.github.io/pyoats-docs/)_\n\n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\n\n<!-- Models -->\n## Models\n\n\n_For more details about the individual models, please refer to the [Documentation](https://georgian-io.github.io/pyoats-docs/) or <a href=\"https://medium.com/georgian-impact-blog/time-series-anomaly-detection-the-detectives-toolbox-9ef131dddaf9\">this blog</a> for deeper explanation._\n\nModel | Type | Multivariate Support* | Requires Fitting | DL Framework Dependency | Paper | Reference Model\n--- | :---: | :---: | :---: | :---: | :---: | :---: \n`ARIMA` | Predictive | \u26a0\ufe0f | \u2705 | | | [`statsmodels.ARIMA`](https://www.statsmodels.org/dev/generated/statsmodels.tsa.arima.model.ARIMA.html)\n`FluxEV` | Predictive | \u26a0\ufe0f | \u2705 | | [\ud83d\udcdd](https://dl.acm.org/doi/10.1145/3437963.3441823) | \n`LightGBM` | Predictive | \u26a0\ufe0f | \u2705 | | | [`darts.LightGBM`](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.gradient_boosted_model.html)\n`Moving Average` | Predictive | \u26a0\ufe0f | | | | \n`N-BEATS` | Predictive | \u2705 | \u2705 | [![Pytorch][Pytorch.org]][Torch-url] | [\ud83d\udcdd](https://openreview.net/forum?id=r1ecqn4YwB) | [`darts.NBEATS`](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nbeats.html)\n`N-HiTS` | Predictive | \u2705 | \u2705 | [![Pytorch][Pytorch.org]][Torch-url] | [\ud83d\udcdd](https://arxiv.org/abs/2201.12886) | [`darts.NHiTS`](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nhits.html)\n`RandomForest` | Predictive | \u26a0\ufe0f | \u2705 | | | [`darts.RandomForest`](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.random_forest.html)\n`Regression` | Predictive | \u26a0\ufe0f | \u2705 | | | [`darts.Regression`](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_model.html)\n`RNN` | Predictive | \u2705 | \u2705 | [![Pytorch][Pytorch.org]][Torch-url] | | [`darts.RNN`](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.rnn_model.html)\n`Temporal Convolution Network` | Predictive | \u2705 | \u2705 | [![Pytorch][Pytorch.org]][Torch-url] | [\ud83d\udcdd](https://arxiv.org/abs/1803.01271) | [`darts.TCN`](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tcn_model.html)\n`Temporal Fusion Transformers` | Predictive | \u2705 | \u2705 | [![Pytorch][Pytorch.org]][Torch-url] | [\ud83d\udcdd](https://arxiv.org/abs/1912.09363) | [`darts.TFT`](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html)\n`Transformer` | Predictive | \u2705 | \u2705 | [![Pytorch][Pytorch.org]][Torch-url] | [\ud83d\udcdd](https://arxiv.org/abs/1706.03762) | [`darts.Transformer`](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.transformer_model.html)\n`Isolation Forest` | Distance-Based | \u2705 | \u2705 | || [`pyod.IForest`](https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.iforest)\n`Matrix Profile` | Distance-Based | \u2705 | | | [\ud83d\udcdd](https://www.cs.ucr.edu/~eamonn/MatrixProfile.html) | [`stumpy`](https://github.com/TDAmeritrade/stumpy)\n`TranAD` | Reconstruction-Based | \u2705 | \u2705 | [![TensorFlow][TensorFlow.org]][Torch-url] | [\ud83d\udcdd](https://arxiv.org/abs/2201.07284) | [`tranad`](https://github.com/imperial-qore/TranAD)\n`Variational Autoencoder` | Reconstruction-Based | \u2705 | \u2705 | [![TensorFlow][TensorFlow.org]][Torch-url] | [\ud83d\udcdd](https://arxiv.org/abs/1312.6114) | [`pyod.VAE`](https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.vae)\n`Quantile` | Rule-Based | \u26a0\ufe0f | | || \n\n\n\n\n\n**\\*** For models with \u26a0\ufe0f, score calculation is done separately along each column. This implicitly assumes independence of covariates, which means that **the resultant anomaly scores do not take into account of inter-variable dependency structures.**\n\n\n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\n\n<!-- ROADMAP -->\n## Roadmap\n\n- [ ] Automatic hyper-parameter tuning\n- [ ] More examples \n- [ ] More preprocessors\n- [ ] More models from `pyod`\n\nSee the [open issues](https://github.com/georgian-io/pyoats/issues) for a full list of proposed features (and known issues).\n\n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\n\n\n\n<!-- CONTRIBUTING -->\n## Contributing\n\nContributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.\n\nIf you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag \"enhancement\".\n\nDon't forget to give the project a star! Thanks again!\n\n1. Fork the Project\n2. Create your Feature Branch (`git checkout -b feature/amazing_feature`)\n3. Commit your Changes (`git commit -m 'Add some amazing_feature'`)\n4. Push to the Branch (`git push origin feature/amazing_feature`)\n5. Open a Pull Request\n\n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\n\n\n\n<!-- LICENSE -->\n## License\n\nDistributed under the Apache 2.0 License. See `LICENSE` for more information.\n\n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\n\n\n\n<!-- CONTACT -->\n## Contact\n\n<div align=\"left\">\n <a href=\"https://www.georgian.io\">\n <img src=\"https://s34372.pcdn.co/wp-content/uploads/2022/03/Georgian_Blue.png\" alt=\"Logo\" width=\"auto\" height=\"80\">\n </a>\n </div>\n\n\n|<!-- -->|<!-- -->|<!-- -->|<!-- -->|\n|---|---|---|---|\n| __Benjamin Ye__ | [![Github][BenGithub]][BenLinkedIn-url] | [![LinkedIn][BenLinkedIn]][BenLinkedIn-url] | [![eMail][eMail]][BenEmail-url] \n\n\nProject Link: [https://github.com/georgian-io/oats](https://github.com/georgian-io/oats)\n\n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\n\n\n\n<!-- ACKNOWLEDGMENTS -->\n## Acknowledgments\nI would like to thank my colleagues from Georgian for all the help and advice provided along the way.\n* [Angeline Yasodhara](mailto:angeline@georgian.io)\n* [Akshay Budhkar](mailto:akshay@georgian.io)\n* [Borna Almasi](mailto:borna@georgian.io)\n* [Parinaz Sobhani](mailto:parinaz@georgian.io)\n* [Rodrigo Ceballos Lentini](mailto:rodrigo@georgian.io)\n\nI'd also like to extend my gratitude to all the contributors at [`Darts`][Darts-url] (for time series predictions) and [`PyOD`][PyOD-url] (for general outlier detection), whose projects have enabled a straight-forward extension into the domain of time series anomaly detection.\n\nFinally, it'll be remiss of me to not mention [DATA Lab @ Rice University](https://cs.rice.edu/~xh37/index.html), whose wonderful [`TODS`][TODS-url] package served as a major inspiration for this project. Please check them out especially if you're looking for AutoML support.\n\n[![Darts][Darts]][Darts-url] [![PyOD][PyOD]][PyOD-url] [![TODS][TODS]][TODS-url]\n\n \n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\n\n\n\n<!-- MARKDOWN LINKS & IMAGES -->\n<!-- https://www.markdownguide.org/basic-syntax/#reference-style-links -->\n[contributors-shield]: https://img.shields.io/github/contributors/georgian-io/oats.svg?style=for-the-badge\n[contributors-url]: https://github.com/georgian-io/pyoats/graphs/contributors\n[forks-shield]: https://img.shields.io/github/forks/georgian-io/contributors.svg?style=for-the-badge\n[forks-url]: https://github.com/georgian-io/pyoats/network/members\n[stars-shield]: https://img.shields.io/github/stars/georgian-io/oats.svg?style=for-the-badge\n[stars-url]: https://github.com/georgian-io/pyoats/stargazers\n[issues-shield]: https://img.shields.io/github/issues/georgian-io/oats.svg?style=for-the-badge\n[issues-url]: https://github.com/georgian-io/pyoats/issues\n[license-shield]: https://img.shields.io/github/license/georgian-io/oats.svg?style=for-the-badge\n[license-url]: https://github.com/georgian-io/pyoats/blob/master/LICENSE\n[last_commit-shield]: https://img.shields.io/github/last-commit/georgian-io/oats.svg?style=for-the-badge\n[last_commit-url]: https://github.com/georgian-io/pyoats/commits/\n\n\n\n<!-- Deps Links -->\n[Python-Poetry.org]: https://img.shields.io/badge/Poetry-60A5FA?style=for-the-badge&logo=poetry&logoColor=white\n[Poetry-url]: https://www.python-poetry.org/\n[Python.org]: https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=python&logoColor=white\n[Python-url]: https://www.python.org/\n[PyTorch.org]: https://img.shields.io/badge/PyTorch-EE4C2C?style=for-the-badge&logo=pytorch&logoColor=white\n[Torch-url]: https://pytorch.org/\n[TensorFlow.org]: https://img.shields.io/badge/TensorFlow-FF6F00?style=for-the-badge&logo=tensorflow&logoColor=white\n[TF-url]: https://www.tensorflow.org/\n[Numpy.org]: https://img.shields.io/badge/Numpy-013243?style=for-the-badge&logo=numpy&logoColor=white\n[Numpy-url]: https://www.numpy.org/\n[Darts]: https://img.shields.io/badge/Repo-Darts-2100FF?style=for-the-badge&logo=github&logoColor=white\n[Darts-url]: https://github.com/unit8co/darts\n[PyOD]: https://img.shields.io/badge/Repo-PyOD-000000?style=for-the-badge&logo=github&logoColor=white\n[PyOD-url]: https://github.com/yzhao062/pyod\n[TODS]: https://img.shields.io/badge/Repo-TODS-29B48C?style=for-the-badge&logo=github&logoColor=white\n[TODS-url]: https://github.com/datamllab/tods\n[Docker.com]: https://img.shields.io/badge/Docker-2496ED?style=for-the-badge&logo=docker&logoColor=white\n[Docker-url]: https://docker.com\n[PyTorchLightning.ai]: https://img.shields.io/badge/lightning-792EE5?style=for-the-badge&logo=pytorchlightning&logoColor=white\n[Lightning-url]: https://www.pytorchlightning.ai/\n\n\n\n[BenLinkedIn]: https://img.shields.io/badge/LinkedIn-0A66C2?style=for-the-badge&logo=linkedin&logoColor=white\n[BenLinkedIn-url]: https://www.linkedin.com/in/benjaminye/\n\n[eMail]: https://img.shields.io/badge/EMail-EA4335?style=for-the-badge&logo=gmail&logoColor=white\n[BenEmail-url]: mailto:benjamin.ye@georgian.io\n\n[BenGithub]: https://img.shields.io/badge/Profile-14334A?style=for-the-badge&logo=github&logoColor=white\n[BenGithub-url]: https://www.linkedin.com/in/benjaminye/\n",
"bugtrack_url": null,
"license": "Apache-2.0",
"summary": "Quick and Easy Time Series Outlier Detection",
"version": "0.1.4",
"project_urls": null,
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "e781c2356b57c3af9907b697e295514393e811889fffb3614c68d2c2683e5ce1",
"md5": "36a448e9eaadd4fd9f494018fa82485a",
"sha256": "c7a958da51e578fda54d71001a4df8ce29319999b6ca31cf03753282d52c8457"
},
"downloads": -1,
"filename": "pyoats-0.1.4-py3-none-any.whl",
"has_sig": false,
"md5_digest": "36a448e9eaadd4fd9f494018fa82485a",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<3.12,>=3.9",
"size": 63385,
"upload_time": "2024-05-06T22:14:21",
"upload_time_iso_8601": "2024-05-06T22:14:21.610884Z",
"url": "https://files.pythonhosted.org/packages/e7/81/c2356b57c3af9907b697e295514393e811889fffb3614c68d2c2683e5ce1/pyoats-0.1.4-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "74f8aea6d2043d930c971aea23cbd22e0a22c9f827b0980e1a0f13e0d459bb1f",
"md5": "fe932d26f9b1f74a6bb003cfde0badf5",
"sha256": "6a20913adfc390161a8e713285cabb2e66d4f703069499306213a34b0358e9dc"
},
"downloads": -1,
"filename": "pyoats-0.1.4.tar.gz",
"has_sig": false,
"md5_digest": "fe932d26f9b1f74a6bb003cfde0badf5",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<3.12,>=3.9",
"size": 48331,
"upload_time": "2024-05-06T22:14:34",
"upload_time_iso_8601": "2024-05-06T22:14:34.078274Z",
"url": "https://files.pythonhosted.org/packages/74/f8/aea6d2043d930c971aea23cbd22e0a22c9f827b0980e1a0f13e0d459bb1f/pyoats-0.1.4.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-05-06 22:14:34",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "pyoats"
}
