https://github.com/statefb/singular-spectrum-transformation

fast implementation of singular spectrum transformation (change point detection algorithm)
https://github.com/statefb/singular-spectrum-transformation

anomaly-detection changepoint python time-series

Last synced: 5 months ago
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fast implementation of singular spectrum transformation (change point detection algorithm)

• Host: GitHub
• URL: https://github.com/statefb/singular-spectrum-transformation
• Owner: statefb
• License: mit
• Created: 2018-05-16T10:23:54.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2018-05-20T15:21:52.000Z (about 8 years ago)
• Last Synced: 2025-09-25T13:42:13.487Z (8 months ago)
• Topics: anomaly-detection, changepoint, python, time-series
• Language: Jupyter Notebook
• Homepage:
• Size: 6.36 MB
• Stars: 51
• Watchers: 0
• Forks: 15
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

          
[![Build Status](https://travis-ci.org/statefb/singular-spectrum-transformation.svg?branch=master)](https://travis-ci.org/statefb/singular-spectrum-transformation)  

# SST (Singular Spectrum Transformation)

A fast implementation of Singular Spectrum Transformation for python.

## What is SST?

A change point detection algorithm.

![example](img/example_step.png)

![example](img/example_freq.png)

see more [examples](./notebooks/examples.ipynb)

## Features

#### fast computation

* efficient algorithm using Lanczos method

* [Numba](http://numba.pydata.org/)

## Installation

```

$pip install fastsst

```

## Basic Usage

```usage.py

from fastsst import SingularSpectrumTransformation

sst = SingularSpectrumTransformation(win_length=30)

"""

note:

 - data must be 1d np.ndarray

 - the first run takes a few seconds for jit compling

"""

score = sst.score_offline(data)

```

## Others

* [Comparison between conventional method(i.e. SVD) and faster method(i.e. lanczos)](./notebooks/comparison_between_svd_and_lanczos.ipynb)

## TODO

* online evaluation

## References

1. Tsuyoshi Ide, Koji Tsuda, Change-Point Detection using Krylov Subspace Learning, SIAM International Conference on Data Mining, pp.515-520, 2007

2. Tsuyoshi Ide, Speeding up Change-Point Detection using Matrix Compression (Japanse), Workshop on Information-Based Induction Sciences, 2006

3. Tsuyoshi Ide, Masashi Sugiyama, Anomaly Detection and Change Detection (Japanse), Kodansha, 2015