dominant_fourier_frequency

dtaianomaly.windowing.dominant_fourier_frequency(X: ndarray, lower_bound: int = 10, upper_bound: int = 1000) → int

Compute the window size by selecting the dominant Fourier frequency.

The Fourier transform decomposes a time series into a weighted sum of sine-waves. These weights are called the fourier coefficients and are related to a specific frequency or period length. The assumption is that the greatest coefficient corresponds to the dominant sine-wave which captures the time series characteristics best. The window size is therefore computed as the period of this dominant sine-wave.

Parameters:

Xarray-like of shape (n_samples,)

Input time series.

lower_boundint, default=10

The lower bound on the automatically computed window size.

upper_boundint, default=1000

The lower bound on the automatically computed window size.

Returns:

int

The computed window size.

Warning

Automatically computing the windwow size only works for univariate time series!

Examples

>>> from dtaianomaly.data import demonstration_time_series
>>> from dtaianomaly.windowing import dominant_fourier_frequency
>>> X, _ = demonstration_time_series()
>>> dominant_fourier_frequency(X)
107