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Atlas Trends API demonstration

Example Segmentation

Introduction

The Atlas Trends API is an implementation of a novel method to cluster RTT time series using nonparametric Bayesian models. The API allows producing humanlike segmentation of RIPE Atlas RTT time series.

This repository contains the following Python notebooks demonstrating the API usage:

Name Description Online Notebook
Atlas Trends API Overview of the API Open In Colab

Citation

M. Mouchet, S. Vaton, T. Chonavel, E. Aben and J. D. Hertog, "Large-Scale Characterization and Segmentation of Internet Path Delays With Infinite HMMs," in IEEE Access, vol. 8, pp. 16771-16784, 2020.

@article{mouchet2019large,
  author={M. {Mouchet} and S. {Vaton} and T. {Chonavel} and E. {Aben} and J. {Den Hertog}},
  journal={IEEE Access},
  title={Large-Scale Characterization and Segmentation of Internet Path Delays With Infinite HMMs},
  year={2020},
  volume={8},
  pages={16771-16784},
  doi={10.1109/ACCESS.2020.2968380},
  ISSN={2169-3536}
}

Getting Started

You can run the notebooks on Google Colab by following the links at the top, or locally by running the following in a terminal:

git clone https://github.com/maxmouchet/atlas-trends-demo.git
cd atlas-trends-demo
python3 -m venv trends-env; source trends-env/bin/activate
pip install -r requirements.txt
jupyter lab