CFDAutoperiod

class pyriodicity.CFDAutoperiod

CFD-Autoperiod periodicity detector.

Find the periods in a given signal or series using CFD-Autoperiod [1].

See also

pyriodicity.Autoperiod

Autoperiod periodicity detector.

References

[1]

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. https://doi.org/10.1007/978-3-030-39098-3_4

Examples

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 CFDAutoperiod to find the list of periods in the data.

>>> from pyriodicity import CFDAutoperiod
>>> CFDAutoperiod.detect(data)
array([12])

You can specify a lower percentile value should you wish for a more lenient detection

>>> CFDAutoperiod.detect(data, percentile=90)
array([12])

You can also increase the number of random data permutations for a more robust power threshold estimation

>>> CFDAutoperiod.detect(data, k=300)
array([12])

CFDAutoperiod is considered a more robust variant of Autoperiod against noise. The detection algorithm found exactly one periodicity length of 12, suggesting a strong yearly periodicity.

Methods

detect(data[, k, percentile, detrend_func, ...])

Find periods in the given series.