roerich 0.3.0

Roerich is a python library for online and offline change point detection in time series data based on machine learning.

Welcome to Roerich

Roerich is a python library for online and offline change point detection in time series data. It was named after the painter Nicholas Roerich, known as the Master of the Mountains. Read more at: https://www.roerich.org.

Fragment of "Himalayas", 1933

Currently, the library contains official implementations of change point detection algorithms based on direct density ratio estimation from the following articles:

• Mikhail Hushchyn and Andrey Ustyuzhanin. “Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation.” J. Comput. Sci. 53 (2021): 101385. [journal] [arxiv]
• Mikhail Hushchyn, Kenenbek Arzymatov and Denis Derkach. “Online Neural Networks for Change-Point Detection.” ArXiv abs/2010.01388 (2020). [arxiv]

Dependencies and install

pip install roerich

or

git clone https://github.com/HSE-LAMBDA/roerich.git
cd roerich
python setup.py install

Basic usage

The following code snippet generates a noisy synthetic data, performs change point detection, and displays the results. If you use own dataset, make sure that it has a shape (seq_len, n_dims).

import roerich
from roerich.algorithms import ChangePointDetectionClassifier

# generate time series
X, cps_true = roerich.generate_dataset(period=200, N_tot=2000)

# detection
cpd = ChangePointDetectionClassifier()
score, cps_pred = cpd.predict(X)

# visualization
roerich.display(X, cps_true, score, cps_pred)

Related libraries

Thanks to all our contributors

License

BSD 2-Clause License

Copyright (c) 2017, ENS Paris-Saclay, CNRS
All rights reserved.

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modification, are permitted provided that the following conditions are met:

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* Redistributions in binary form must reproduce the above copyright notice,
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