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OATS

Quick and Easy Outlier Detection for Time Series
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
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

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Getting Started

Prerequisites

>= 3.8

For Docker Install:

For Local Install:

Installation

PyPI

1. Install package via pip

   pip install pyoats
   

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

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

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

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.

• Angeline Yasodhara

• Akshay Budhkar

• Borna Almasi

• Parinaz Sobhani

• Rodrigo Ceballos Lentini

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.

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