pyoats 0.1.4
Quick and Easy Time Series Outlier Detection
License: Apache-2.0
Requires Python: <3.12,>=3.9

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<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>
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<!-- TABLE OF CONTENTS -->
<details>
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  <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.


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### 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]


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<!-- 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
    ```



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<!-- 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/)_

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<!-- 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.**


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<!-- 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).

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## 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

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<!-- LICENSE -->
## License

Distributed under the Apache 2.0 License. See `LICENSE` for more information.

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  <a href="https://www.georgian.io">
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|<!-- -->|<!-- -->|<!-- -->|<!-- -->|
|---|---|---|---|
| __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)

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<!-- 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]

 
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