# skchange
`skchange` provies sktime-compatible change detection and changepoint-based anomaly detection algorithms. Methods implement the annotator interface of sktime.
A playground for now.
[](https://codecov.io/gh/NorskRegnesentral/skchange)
[](https://github.com/NorskRegnesentral/skchange/actions/workflows/tests.yaml)
[](https://github.com/sktime/sktime/blob/main/LICENSE)
[](https://github.com/psf/black)
## Installation
```sh
pip install skchange
```
Requires Python >= 3.8, < 3.13.
## Quickstart
### Changepoint detection / time series segmentation
```python
from skchange.change_detectors.moscore import Moscore
from skchange.datasets.generate import generate_teeth_data
df = generate_teeth_data(n_segments=10, segment_length=50, mean=5, random_state=1)
detector = Moscore(bandwidth=10, fmt="sparse")
detector.fit_predict(df)
>>>
0 49
1 99
2 149
3 199
4 249
5 299
6 349
7 399
8 449
Name: changepoints, dtype: int32
```
### Multivariate anomaly detection
```python
from skchange.anomaly_detectors.mvcapa import Mvcapa
from skchange.datasets.generate import generate_teeth_data
df = generate_teeth_data(
n_segments=5,
segment_length=50,
p=10,
mean=10,
affected_proportion=0.2,
random_state=2,
)
detector = Mvcapa(collective_penalty="sparse", fmt="sparse")
detector.fit_predict(df)
>>>
start end components
0 50 99 [0, 1]
1 150 199 [0, 1]
```
<!-- Optional dependencies:
- Penalty tuning: `optuna` >= 3.1.1
- Plotting: `plotly` >= 5.13.0. -->
## 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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