<div align="center">
<h1>Pyriodicity</h1>
[](https://pypi.org/project/pyriodicity/)
[](https://codecov.io/gh/iskandergaba/pyriodicity)
[](https://pyriodicity.readthedocs.io/en/latest)
[](https://github.com/iskandergaba/pyriodicity/actions/workflows/ci.yml)
</div>
## About Pyriodicity
Pyriodicity provides an intuitive and easy-to-use Python implementation for periodicity detection in univariate signals. Pyriodicity supports the following detection methods:
- [Autocorrelation Function (ACF)](https://otexts.com/fpp3/acf.html)
- [Autoperiod](https://doi.org/10.1137/1.9781611972757.40)
- [CFD-Autoperiod](https://doi.org/10.1007/978-3-030-39098-3_4)
- [Fast Fourier Transform (FFT)](https://otexts.com/fpp3/useful-predictors.html#fourier-series)
- [RobustPeriod](https://doi.org/10.1145/3448016.3452779)
## Installation
To install the latest version of `pyriodicity`, simply run:
```shell
pip install pyriodicity
```
## Usage
Please refer to the [package documentation](https://pyriodicity.readthedocs.io) for more information.
For this example, start by loading Mauna Loa Weekly Atmospheric CO2 Data from [`statsmodels`](https://www.statsmodels.org) and downsampling its data to a monthly frequency.
```python
>>> from statsmodels.datasets import co2
>>> data = co2.load().data
>>> data = data.resample("ME").mean().ffill()
```
Use `Autoperiod` to find the list of periods based in this data (if any).
```python
>>> from pyriodicity import Autoperiod
>>> Autoperiod.detect(data)
array([12])
```
The detected periodicity length is 12 which suggests a strong yearly seasonality given that the data has a monthly frequency.
All the supported estimation algorithms can be used in the same manner as in the example above with different optional parameters. Check the [API Reference](https://pyriodicity.readthedocs.io/en/stable/api.html) for more details.
## References
- [1] Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. [OTexts.com/fpp3](https://otexts.com/fpp3). Accessed on 09-15-2024.
- [2] Vlachos, M., Yu, P., & Castelli, V. (2005). On periodicity detection and Structural Periodic similarity. Proceedings of the 2005 SIAM International Conference on Data Mining. [doi.org/10.1137/1.9781611972757.40](https://doi.org/10.1137/1.9781611972757.40).
- [3] Puech, T., Boussard, M., D'Amato, A., & Millerand, G. (2020). A fully automated periodicity detection in time series. In Advanced Analytics and Learning on Temporal Data: 4th ECML PKDD Workshop, AALTD 2019, Würzburg, Germany, September 20, 2019, Revised Selected Papers 4 (pp. 43-54). Springer International Publishing. [doi.org/10.1007/978-3-030-39098-3_4](https://doi.org/10.1007/978-3-030-39098-3_4).
- [4] Wen, Q., He, K., Sun, L., Zhang, Y., Ke, M., & Xu, H. (2021, June). RobustPeriod: Robust time-frequency mining for multiple periodicity detection. In Proceedings of the 2021 international conference on management of data (pp. 2328-2337). [https://doi.org/10.1145/3448016.3452779](https://doi.org/10.1145/3448016.3452779).
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"description": "<div align=\"center\">\n<h1>Pyriodicity</h1>\n\n[](https://pypi.org/project/pyriodicity/)\n\n\n[](https://codecov.io/gh/iskandergaba/pyriodicity)\n[](https://pyriodicity.readthedocs.io/en/latest)\n[](https://github.com/iskandergaba/pyriodicity/actions/workflows/ci.yml)\n</div>\n\n## About Pyriodicity\nPyriodicity provides an intuitive and easy-to-use Python implementation for periodicity detection in univariate signals. Pyriodicity supports the following detection methods:\n- [Autocorrelation Function (ACF)](https://otexts.com/fpp3/acf.html)\n- [Autoperiod](https://doi.org/10.1137/1.9781611972757.40)\n- [CFD-Autoperiod](https://doi.org/10.1007/978-3-030-39098-3_4)\n- [Fast Fourier Transform (FFT)](https://otexts.com/fpp3/useful-predictors.html#fourier-series)\n- [RobustPeriod](https://doi.org/10.1145/3448016.3452779)\n\n## Installation\nTo install the latest version of `pyriodicity`, simply run:\n\n```shell\npip install pyriodicity\n```\n\n## Usage\nPlease refer to the [package documentation](https://pyriodicity.readthedocs.io) for more information.\n\nFor this example, start by loading Mauna Loa Weekly Atmospheric CO2 Data from [`statsmodels`](https://www.statsmodels.org) and downsampling its data to a monthly frequency.\n```python\n>>> from statsmodels.datasets import co2\n>>> data = co2.load().data\n>>> data = data.resample(\"ME\").mean().ffill()\n```\n\nUse `Autoperiod` to find the list of periods based in this data (if any).\n```python\n>>> from pyriodicity import Autoperiod\n>>> Autoperiod.detect(data)\narray([12])\n```\n\nThe detected periodicity length is 12 which suggests a strong yearly seasonality given that the data has a monthly frequency.\n\nAll the supported estimation algorithms can be used in the same manner as in the example above with different optional parameters. Check the [API Reference](https://pyriodicity.readthedocs.io/en/stable/api.html) for more details.\n\n## References\n- [1] Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. [OTexts.com/fpp3](https://otexts.com/fpp3). Accessed on 09-15-2024.\n- [2] Vlachos, M., Yu, P., & Castelli, V. (2005). On periodicity detection and Structural Periodic similarity. Proceedings of the 2005 SIAM International Conference on Data Mining. [doi.org/10.1137/1.9781611972757.40](https://doi.org/10.1137/1.9781611972757.40).\n- [3] Puech, T., Boussard, M., D'Amato, A., & Millerand, G. (2020). A fully automated periodicity detection in time series. In Advanced Analytics and Learning on Temporal Data: 4th ECML PKDD Workshop, AALTD 2019, W\u00fcrzburg, Germany, September 20, 2019, Revised Selected Papers 4 (pp. 43-54). Springer International Publishing. [doi.org/10.1007/978-3-030-39098-3_4](https://doi.org/10.1007/978-3-030-39098-3_4).\n- [4] Wen, Q., He, K., Sun, L., Zhang, Y., Ke, M., & Xu, H. (2021, June). RobustPeriod: Robust time-frequency mining for multiple periodicity detection. In Proceedings of the 2021 international conference on management of data (pp. 2328-2337). [https://doi.org/10.1145/3448016.3452779](https://doi.org/10.1145/3448016.3452779).\n",
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