# skchange
[](https://codecov.io/gh/NorskRegnesentral/skchange)
[](https://github.com/NorskRegnesentral/skchange/actions/workflows/tests.yaml)
[](https://skchange.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/sktime/sktime/blob/main/LICENSE)
[](https://github.com/psf/black)
[](https://pypi.org/project/skchange/)
[skchange]((https://skchange.readthedocs.io/en/latest/)) provides [sktime](https://www.sktime.net/)-compatible change detection and changepoint-based anomaly detection algorithms.
Experimental but maturing.
## Documentation
* [Documentation](https://skchange.readthedocs.io/)
* [Notebook tutorial](https://github.com/sktime/sktime-tutorial-pydata-global-2024)
## Installation
It is recommended to install skchange with [numba](https://numba.readthedocs.io/en/stable/) for faster performance:
```sh
pip install skchange[numba]
```
Alternatively, you can install skchange without numba:
```sh
pip install skchange
```
## Quickstart
### Changepoint detection / time series segmentation
```python
from skchange.change_detectors.moving_window import MovingWindow
from skchange.datasets.generate import generate_alternating_data
df = generate_alternating_data(n_segments=10, segment_length=50, mean=5, random_state=1)
detector = MovingWindow(bandwidth=10)
detector.fit_predict(df)
```
```python
ilocs
0 50
1 100
2 150
3 200
4 250
5 300
6 350
7 400
8 450
```
### Multivariate anomaly detection
```python
import numpy as np
from skchange.anomaly_detectors import MVCAPA
from skchange.datasets.generate import generate_anomalous_data
n = 300
anomalies = [(100, 120), (250, 300)]
means = [[8.0, 0.0, 0.0], [2.0, 3.0, 5.0]]
df = generate_anomalous_data(n, anomalies, means, random_state=3)
detector = MVCAPA()
detector.fit_predict(df)
```
```python
ilocs labels icolumns
0 [100, 120) 1 [0]
1 [250, 300) 2 [2, 1, 0]
```
## License
skchange is a free and open-source software licensed under the [BSD 3-clause license](https://github.com/NorskRegnesentral/skchange/blob/main/LICENSE).
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