Anomaly detection library based on singular spectrum transformation(sst)
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tsurubee Merge pull request #6 from tsurubee/krylov-subspace
Accelerated the computation of singular spectrum transformation by Krylov subspace
Latest commit 183e7ec Feb 3, 2019


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Banpei is a Python package of the anomaly detection.
Anomaly detection is a technique used to identify unusual patterns that do not conform to expected behavior.


Python 3.x (2.x is not supported)


Use the command:

$ git clone
$ cd banpei
$ pip install .

After installation, you can import banpei in Python.

$ python
>>> import banpei



Singular spectrum transformation(sst)

import banpei 
model   = banpei.SST(w=50)
results = model.detect(data)

The input 'data' must be one-dimensional array-like object containing a sequence of values.
The output 'results' is Numpy array with the same size as input data.
The graph below shows the change-point scoring calculated by sst for the periodic data.


The data used is placed as '/tests/test_data/periodic_wave.csv'. You can read a CSV file using the following code.

import pandas as pd
raw_data = pd.read_csv('./tests/test_data/periodic_wave.csv')
data = raw_data['y']

SST processing can be accelerated using the Lanczos method which is one of Krylov subspace methods by specifying True for the is_lanczos argument like below.

results = model.detect(data, is_lanczos=True)

Real-time monitoring with Bokeh

Banpei is developed with the goal of constructing the environment of real-time abnormality monitoring. In order to achieve the goal, Banpei has the function corresponded to the streaming data. With the help of Bokeh, which is great visualization library, we can construct the simple monitoring tool.
Here's a simple demonstration movie of change-point detection of the data trends.

sst detection
The sample code how to construct real-time monitoring environment is placed in '/demo' folder.

The implemented algorithm

Outlier detection

  • Hotelling's theory

Change point detection

  • Singular spectrum transformation(sst)


This project is licensed under the terms of the MIT license, see LICENSE.