Pyriodicity provides an intuitive and easy-to-use Python implementation for periodicity detection in univariate signals. Pyriodicity supports the following detection methods:
To install the latest version of pyriodicity, simply run:
pip install pyriodicityPlease refer to the package documentation for more information.
For this example, start by loading Mauna Loa Weekly Atmospheric CO2 Data from statsmodels and downsampling its data to a monthly frequency.
>>> 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).
>>> from pyriodicity import Autoperiod
>>> autoperiod = Autoperiod(data)
>>> autoperiod.fit()
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 for more details.
- [1] Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. 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.
- [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.