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OATS

Quick and Easy Outlier Detection for Time Series
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Table of Contents
  1. About The Project
  2. Getting Started
  3. Usage
  4. Models
  5. Roadmap
  6. Contributing
  7. License
  8. Contact
  9. Acknowledgments

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 Poetry

Pytorch PytorchLightning TensorFlow Numpy

Darts PyOD

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Getting Started


Usage Example

Prerequisites

Python >=3.8, <3.11

For Docker Install:

Docker

For Local Install:

Poetry

Installation

PyPI

  1. Install package via pip
    pip install pyoats
    ❗ Installing using an environment manager such as conda, venv, and poetry is highly encouraged as this package contains deep learning frameworks.

Docker

  1. Clone the repo
    git clone https://github.com/georgian-io/pyoats.git && cd pyoats 
  2. Build image
    docker build -t pyoats . 
  3. Run Container
    # CPU Only
    docker run -it pyoats
    
    # with GPU
    docker run -it --gpus all pyoats

Local

  1. Clone the repo
    git clone https://github.com/georgian-io/pyoats.git && cd pyoats 
  2. Install via Poetry
    poetry install

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Usage

Quick Start

For a quick start, please refer to our blog or copy our Colab notebook!

Getting Anomaly Score

from oats.models import NHiTSModel

model = NHiTSModel(window=20, use_gpu=True)
model.fit(train)
scores = model.get_scores(test)

Getting Threshold

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

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Models

For more details about the individual models, please refer to the Documentation or this blog for deeper explanation.

Model Type Multivariate Support* Requires Fitting DL Framework Dependency Paper Reference Model
ARIMA Predictive ⚠️ statsmodels.ARIMA
FluxEV Predictive ⚠️ 📝
LightGBM Predictive ⚠️ darts.LightGBM
Moving Average Predictive ⚠️
N-BEATS Predictive Pytorch 📝 darts.NBEATS
N-HiTS Predictive Pytorch 📝 darts.NHiTS
RandomForest Predictive ⚠️ darts.RandomForest
Regression Predictive ⚠️ darts.Regression
RNN Predictive Pytorch darts.RNN
Temporal Convolution Network Predictive Pytorch 📝 darts.TCN
Temporal Fusion Transformers Predictive Pytorch 📝 darts.TFT
Transformer Predictive Pytorch 📝 darts.Transformer
Isolation Forest Distance-Based pyod.IForest
Matrix Profile Distance-Based 📝 stumpy
TranAD Reconstruction-Based TensorFlow 📝 tranad
Variational Autoencoder Reconstruction-Based TensorFlow 📝 pyod.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

  • Automatic hyper-parameter tuning
  • More examples
  • More preprocessors
  • More models from pyod

See the open 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

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

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Contact

Benjamin Ye Github LinkedIn eMail

Project Link: https://github.com/georgian-io/oats

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Acknowledgments

I would like to thank my colleagues from Georgian for all the help and advice provided along the way.

I'd also like to extend my gratitude to all the contributors at Darts (for time series predictions) and PyOD (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, whose wonderful TODS package served as a major inspiration for this project. Please check them out especially if you're looking for AutoML support.

Darts PyOD TODS

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