pyriodicity 0.4.4

Pyriodicity provides an intuitive and easy-to-use Python implementation for periodicity detection in univariate signals.

Pyriodicity

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)
• Autoperiod
• CFD-Autoperiod
• Fast Fourier Transform (FFT)
• RobustPeriod

Installation

To install pyriodicity, simply run:

pip install pyriodicity

To install the latest development version, you can run:

pip install git+https://github.com/iskandergaba/pyriodicity.git

Usage

Please 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.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 for more details.

References

• [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.
• [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.