Welcome to Roerich

Roerich is a python library for online and offline change point detection for time series analysis, signal processing, and segmentation. 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
pip install -e .

Basic usage

(See more examples in the documentation.)

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)

Support

• Home: https://github.com/HSE-LAMBDA/roerich
• Documentation: https://hse-lambda.github.io/roerich
• For any usage questions, suggestions and bugs use the issue page, please.

Related libraries

Thanks to all our contributors

License

BSD 2-Clause License

Copyright (c) 2020 Laboratory of methods for Big Data Analysis at HSE
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
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* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

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