Pyriodicity#
Pyriodicity provides an intuitive and efficient Python implementation of periodicity length detection methods in univariate signals. You can check the supported detection methods in the API Reference.
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
References#
Hyndman, R.J., & Athanasopoulos, G. (2021). Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3. Accessed on 09-15-2024.
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.
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.
Toller, M., Santos, T., & Kern, R. (2019). SAZED: parameter-free domain-agnostic season length estimation in time series data. Data Mining and Knowledge Discovery, 33(6), 1775-1798. doi.org/10.1007/s10618-019-00645-z.
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). doi.org/10.1145/3448016.3452779.