| Name | dtaianomaly JSON |
| Version |
0.2.3
JSON |
| download |
| home_page | None |
| Summary | A simple-to-use Python package for time series anomaly detection! |
| upload_time | 2024-12-02 16:55:38 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <=3.12,>=3.8 |
| license | MIT License Copyright (c) 2023-2025 KU Leuven, DTAI Research Group Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
machine-learning
time-series
anomaly-detection
data-mining
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
numpy
scipy
numba
stumpy
scikit-learn
pandas
matplotlib
statsmodels
pyod
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Dtaianomaly for Time Series Anomaly Detection
[](https://dtaianomaly.readthedocs.io/en/stable/?badge=stable)
[](https://pypi.org/project/dtaianomaly/)
[](https://pepy.tech/project/dtaianomaly)
[](https://pypi.python.org/pypi/dtaianomaly/)
[](https://pypi.python.org/pypi/dtaianomaly/)
A simple-to-use Python package for the development and analysis of time series anomaly
detection techniques. Here we describe the main usage of `dtaianomaly`, but be sure to
check out the [documentation](https://dtaianomaly.readthedocs.io/en/stable/index.html)
for more information.
## Installation
The preferred way to install `dtaianomaly` is via PyPi. See the [documentation](https://dtaianomaly.readthedocs.io/en/stable/index.html)
for more options.
```
pip install dtaianomaly
```
## Features
The three key features of `dtaianomaly` are as follows:
1. State-of-the-art time series anomaly detection via a simple API.
[Learn more!](https://dtaianomaly.readthedocs.io/en/stable/getting_started/anomaly_detection.html)
2. Develop custom models for anomaly detection.
[Learn more!](https://dtaianomaly.readthedocs.io/en/stable/getting_started/custom_models.html)
3. Quantitative evaluation of time series anomaly detection.
[Learn more!](https://dtaianomaly.readthedocs.io/en/stable/getting_started/quantitative_evaluation.html)
## Example
Below code shows a simple example of `dtaianomaly`, which is part of the
[anomaly detection notebook](notebooks/Anomaly-detection.ipynb). More examples
are provided in the [other notebooks](notebooks) and in the
[documentation](https://dtaianomaly.readthedocs.io/en/stable/index.html).
```python
from dtaianomaly.data import demonstration_time_series
from dtaianomaly.preprocessing import MovingAverage
from dtaianomaly.anomaly_detection import MatrixProfileDetector
# Load the data
X, y = demonstration_time_series()
# Preprocess the data using a moving average
preprocessor = MovingAverage(window_size=10)
X_, _ = preprocessor.fit_transform(X)
# Fit the matrix profile detector on the processed data
detector = MatrixProfileDetector(window_size=100)
detector.fit(X_)
# Compute either the decision scores, specific to the detector, or the anomaly probabilities
decision_scores = detector.decision_function(X_)
anomaly_probabilities = detector.predict_proba(X_)
```
## Contribute
The goal of ``dtaianomaly`` is to be community-driven. All types of contributions
are welcome. This includes code, but also bug reports, improvements to the documentation,
additional tests and more. Check out [the documentation](https://dtaianomaly.readthedocs.io/en/stable/additional_information/contributing.html)
to find more information about how you can contribute!
## License
Copyright (c) 2023-2025 KU Leuven, DTAI Research Group
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
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"description": "# Dtaianomaly for Time Series Anomaly Detection\r\n\r\n[](https://dtaianomaly.readthedocs.io/en/stable/?badge=stable)\r\n[](https://pypi.org/project/dtaianomaly/)\r\n[](https://pepy.tech/project/dtaianomaly)\r\n[](https://pypi.python.org/pypi/dtaianomaly/)\r\n[](https://pypi.python.org/pypi/dtaianomaly/)\r\n\r\nA simple-to-use Python package for the development and analysis of time series anomaly \r\ndetection techniques. Here we describe the main usage of `dtaianomaly`, but be sure to\r\ncheck out the [documentation](https://dtaianomaly.readthedocs.io/en/stable/index.html) \r\nfor more information. \r\n\r\n## Installation\r\n\r\nThe preferred way to install `dtaianomaly` is via PyPi. See the [documentation](https://dtaianomaly.readthedocs.io/en/stable/index.html) \r\nfor more options.\r\n```\r\npip install dtaianomaly\r\n```\r\n\r\n## Features\r\n\r\nThe three key features of `dtaianomaly` are as follows:\r\n1. State-of-the-art time series anomaly detection via a simple API.\r\n [Learn more!](https://dtaianomaly.readthedocs.io/en/stable/getting_started/anomaly_detection.html)\r\n2. Develop custom models for anomaly detection.\r\n [Learn more!](https://dtaianomaly.readthedocs.io/en/stable/getting_started/custom_models.html)\r\n3. Quantitative evaluation of time series anomaly detection.\r\n [Learn more!](https://dtaianomaly.readthedocs.io/en/stable/getting_started/quantitative_evaluation.html)\r\n\r\n## Example\r\n\r\nBelow code shows a simple example of `dtaianomaly`, which is part of the \r\n[anomaly detection notebook](notebooks/Anomaly-detection.ipynb). More examples \r\nare provided in the [other notebooks](notebooks) and in the \r\n[documentation](https://dtaianomaly.readthedocs.io/en/stable/index.html).\r\n\r\n```python\r\nfrom dtaianomaly.data import demonstration_time_series\r\nfrom dtaianomaly.preprocessing import MovingAverage\r\nfrom dtaianomaly.anomaly_detection import MatrixProfileDetector\r\n\r\n# Load the data\r\nX, y = demonstration_time_series()\r\n\r\n# Preprocess the data using a moving average\r\npreprocessor = MovingAverage(window_size=10)\r\nX_, _ = preprocessor.fit_transform(X)\r\n\r\n# Fit the matrix profile detector on the processed data\r\ndetector = MatrixProfileDetector(window_size=100)\r\ndetector.fit(X_)\r\n\r\n# Compute either the decision scores, specific to the detector, or the anomaly probabilities\r\ndecision_scores = detector.decision_function(X_)\r\nanomaly_probabilities = detector.predict_proba(X_)\r\n```\r\n\r\n\r\n## Contribute\r\n\r\nThe goal of ``dtaianomaly`` is to be community-driven. All types of contributions\r\nare welcome. This includes code, but also bug reports, improvements to the documentation,\r\nadditional tests and more. Check out [the documentation](https://dtaianomaly.readthedocs.io/en/stable/additional_information/contributing.html)\r\nto find more information about how you can contribute!\r\n\r\n## License\r\n\r\n Copyright (c) 2023-2025 KU Leuven, DTAI Research Group\r\n \r\n Permission is hereby granted, free of charge, to any person obtaining a copy\r\n of this software and associated documentation files (the \"Software\"), to deal\r\n in the Software without restriction, including without limitation the rights\r\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\r\n copies of the Software, and to permit persons to whom the Software is\r\n furnished to do so, subject to the following conditions:\r\n \r\n The above copyright notice and this permission notice shall be included in all\r\n copies or substantial portions of the Software.\r\n \r\n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\r\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\r\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\r\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\r\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\r\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\r\n SOFTWARE.\r\n",
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